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1Starting GRPO training with 5 reward functions..., iters: 500
2
3=== Validation Sample Details ===
4
5📋 Raw Prompt:
6There are 25 roses in a garden. There are 40 tulips. There are 35 daisies. What percentage of flowers are not roses?.
7
8==========
9
10
11🔄 Model Input:
12<|im_start|>system
13A conversation between User and Assistant. The user asks a question, and the Assistant solves it. The assistant first thinks about the reasoning process in the mind and then provides the user with the answer. The reasoning process and answer are enclosed within <think> </think> and <answer> </answer> tags, respectively, i.e., <think> reasoning process here </think><answer> answer here </answer>.<|im_end|>
14<|im_start|>user
15There are 25 roses in a garden. There are 40 tulips. There are 35 daisies. What percentage of flowers are not roses?.<|im_end|>
16<|im_start|>assistant
17
18
19==========
20
21
22📝 Generation:
23<think> Roses: 25 (25% of 100). Tulips: 40 (80% of 100). Daisies: 35 (70% of 50). None are: 5 (5% of 100). Percentage of flowers that are not roses: (80/100) * 100 = 0.8*100 = 80% </think><answer>0.8</answer>
24
25==========
26
27
28✅ Answer:
2975
30
31==========
32
33
34🔍 Extracted Answer:
350.8
36
37===================================
38
39Iter 1: Val loss 0.000, Val total_rewards_mean 0.688, Val total_rewards_std 0.400, Val grouped_rewards_mean 0.688, Val grouped_rewards_std 0.400, Val kl 0.001, Val r1_accuracy_reward_func_mean 0.000, Val r1_accuracy_reward_func_std 0.000, Val r1_int_reward_func_mean 0.000, Val r1_int_reward_func_std 0.000, Val r1_strict_format_reward_func_mean 0.125, Val r1_strict_format_reward_func_std 0.217, Val r1_soft_format_reward_func_mean 0.375, Val r1_soft_format_reward_func_std 0.217, Val r1_count_xml_mean 0.188, Val r1_count_xml_std 0.188, Val took 31.516s
40Iter 10: Train loss 0.000, Total rewards mean 1.086, Total rewards std 0.723, Grouped rewards mean 1.086, Grouped rewards std 0.723, KL 0.001, r1_accuracy_reward_func mean 0.200, r1_accuracy_reward_func std 0.346, r1_int_reward_func mean 0.100, r1_int_reward_func std 0.137, r1_strict_format_reward_func mean 0.112, r1_strict_format_reward_func std 0.122, r1_soft_format_reward_func mean 0.425, r1_soft_format_reward_func std 0.112, r1_count_xml mean 0.249, r1_count_xml std 0.136, Learning Rate 1.000e-05, It/sec 0.028, Tokens/sec 18.284, Peak mem 15.596 GB
41Iter 20: Train loss -0.002, Total rewards mean 2.083, Total rewards std 1.205, Grouped rewards mean 2.083, Grouped rewards std 1.205, KL 0.003, r1_accuracy_reward_func mean 0.300, r1_accuracy_reward_func std 0.520, r1_int_reward_func mean 0.175, r1_int_reward_func std 0.230, r1_strict_format_reward_func mean 0.225, r1_strict_format_reward_func std 0.273, r1_soft_format_reward_func mean 0.850, r1_soft_format_reward_func std 0.205, r1_count_xml mean 0.533, r1_count_xml std 0.228, Learning Rate 1.000e-05, It/sec 0.036, Tokens/sec 18.647, Peak mem 15.596 GB
42Iter 20: Saved adapter weights to /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/adapters.safetensors and /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/0000020_adapters.safetensors.
43Iter 30: Train loss -0.001, Total rewards mean 3.324, Total rewards std 1.703, Grouped rewards mean 3.324, Grouped rewards std 1.703, KL 0.007, r1_accuracy_reward_func mean 0.500, r1_accuracy_reward_func std 0.720, r1_int_reward_func mean 0.287, r1_int_reward_func std 0.302, r1_strict_format_reward_func mean 0.438, r1_strict_format_reward_func std 0.481, r1_soft_format_reward_func mean 1.288, r1_soft_format_reward_func std 0.295, r1_count_xml mean 0.812, r1_count_xml std 0.343, Learning Rate 1.000e-05, It/sec 0.037, Tokens/sec 18.429, Peak mem 15.596 GB
44Iter 40: Train loss -0.004, Total rewards mean 4.346, Total rewards std 2.036, Grouped rewards mean 4.346, Grouped rewards std 2.036, KL 0.013, r1_accuracy_reward_func mean 0.550, r1_accuracy_reward_func std 0.806, r1_int_reward_func mean 0.350, r1_int_reward_func std 0.392, r1_strict_format_reward_func mean 0.637, r1_strict_format_reward_func std 0.661, r1_soft_format_reward_func mean 1.737, r1_soft_format_reward_func std 0.381, r1_count_xml mean 1.071, r1_count_xml std 0.497, Learning Rate 1.000e-05, It/sec 0.035, Tokens/sec 18.113, Peak mem 15.596 GB
45Iter 40: Saved adapter weights to /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/adapters.safetensors and /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/0000040_adapters.safetensors.
46
47=== Validation Sample Details ===
48
49📋 Raw Prompt:
50There are 25 roses in a garden. There are 40 tulips. There are 35 daisies. What percentage of flowers are not roses?.
51
52==========
53
54
55🔄 Model Input:
56<|im_start|>system
57A conversation between User and Assistant. The user asks a question, and the Assistant solves it. The assistant first thinks about the reasoning process in the mind and then provides the user with the answer. The reasoning process and answer are enclosed within <think> </think> and <answer> </answer> tags, respectively, i.e., <think> reasoning process here </think><answer> answer here </answer>.<|im_end|>
58<|im_start|>user
59There are 25 roses in a garden. There are 40 tulips. There are 35 daisies. What percentage of flowers are not roses?.<|im_end|>
60<|im_start|>assistant
61
62
63==========
64
65
66📝 Generation:
67<think> These are the flowers that are roses: 25 roses. These are the roses that are not roses: 25-4=21. These are the flowers total: 25+40+35=95. The percentage are not roses: 21/95*100=22% </think><answer> 22% </answer>
68
69==========
70
71
72✅ Answer:
7375
74
75==========
76
77
78🔍 Extracted Answer:
7922%
80
81===================================
82
83Iter 50: Val loss -0.016, Val total_rewards_mean 1.156, Val total_rewards_std 0.379, Val grouped_rewards_mean 1.156, Val grouped_rewards_std 0.379, Val kl 0.012, Val r1_accuracy_reward_func_mean 0.000, Val r1_accuracy_reward_func_std 0.000, Val r1_int_reward_func_mean 0.000, Val r1_int_reward_func_std 0.000, Val r1_strict_format_reward_func_mean 0.375, Val r1_strict_format_reward_func_std 0.217, Val r1_soft_format_reward_func_mean 0.500, Val r1_soft_format_reward_func_std 0.000, Val r1_count_xml_mean 0.281, Val r1_count_xml_std 0.162, Val took 21.807s
84Iter 50: Train loss -0.013, Total rewards mean 5.096, Total rewards std 2.520, Grouped rewards mean 5.096, Grouped rewards std 2.520, KL 0.020, r1_accuracy_reward_func mean 0.600, r1_accuracy_reward_func std 0.893, r1_int_reward_func mean 0.387, r1_int_reward_func std 0.456, r1_strict_format_reward_func mean 0.775, r1_strict_format_reward_func std 0.795, r1_soft_format_reward_func mean 2.075, r1_soft_format_reward_func std 0.540, r1_count_xml mean 1.258, r1_count_xml std 0.646, Learning Rate 1.000e-05, It/sec 0.190, Tokens/sec 124.523, Peak mem 15.609 GB
85Iter 60: Train loss -0.036, Total rewards mean 6.138, Total rewards std 2.815, Grouped rewards mean 6.138, Grouped rewards std 2.815, KL 0.040, r1_accuracy_reward_func mean 0.600, r1_accuracy_reward_func std 0.893, r1_int_reward_func mean 0.475, r1_int_reward_func std 0.571, r1_strict_format_reward_func mean 0.938, r1_strict_format_reward_func std 0.935, r1_soft_format_reward_func mean 2.550, r1_soft_format_reward_func std 0.583, r1_count_xml mean 1.575, r1_count_xml std 0.740, Learning Rate 1.000e-05, It/sec 0.039, Tokens/sec 17.662, Peak mem 15.609 GB
86Iter 60: Saved adapter weights to /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/adapters.safetensors and /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/0000060_adapters.safetensors.
87Iter 70: Train loss -0.028, Total rewards mean 7.343, Total rewards std 3.399, Grouped rewards mean 7.343, Grouped rewards std 3.399, KL 0.072, r1_accuracy_reward_func mean 0.750, r1_accuracy_reward_func std 1.079, r1_int_reward_func mean 0.587, r1_int_reward_func std 0.705, r1_strict_format_reward_func mean 1.175, r1_strict_format_reward_func std 1.181, r1_soft_format_reward_func mean 2.987, r1_soft_format_reward_func std 0.673, r1_count_xml mean 1.843, r1_count_xml std 0.863, Learning Rate 1.000e-05, It/sec 0.026, Tokens/sec 17.761, Peak mem 15.609 GB
88Iter 80: Train loss -1.025, Total rewards mean 8.592, Total rewards std 3.904, Grouped rewards mean 8.592, Grouped rewards std 3.904, KL 0.134, r1_accuracy_reward_func mean 0.850, r1_accuracy_reward_func std 1.253, r1_int_reward_func mean 0.738, r1_int_reward_func std 0.823, r1_strict_format_reward_func mean 1.438, r1_strict_format_reward_func std 1.339, r1_soft_format_reward_func mean 3.438, r1_soft_format_reward_func std 0.741, r1_count_xml mean 2.130, r1_count_xml std 0.987, Learning Rate 1.000e-05, It/sec 0.038, Tokens/sec 17.489, Peak mem 15.609 GB
89Iter 80: Saved adapter weights to /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/adapters.safetensors and /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/0000080_adapters.safetensors.
90Iter 90: Train loss -0.747, Total rewards mean 9.777, Total rewards std 4.470, Grouped rewards mean 9.777, Grouped rewards std 4.470, KL 0.332, r1_accuracy_reward_func mean 0.950, r1_accuracy_reward_func std 1.426, r1_int_reward_func mean 0.837, r1_int_reward_func std 0.960, r1_strict_format_reward_func mean 1.675, r1_strict_format_reward_func std 1.554, r1_soft_format_reward_func mean 3.912, r1_soft_format_reward_func std 0.785, r1_count_xml mean 2.402, r1_count_xml std 1.089, Learning Rate 1.000e-05, It/sec 0.033, Tokens/sec 18.673, Peak mem 15.609 GB
91
92=== Validation Sample Details ===
93
94📋 Raw Prompt:
95There are 25 roses in a garden. There are 40 tulips. There are 35 daisies. What percentage of flowers are not roses?.
96
97==========
98
99
100🔄 Model Input:
101<|im_start|>system
102A conversation between User and Assistant. The user asks a question, and the Assistant solves it. The assistant first thinks about the reasoning process in the mind and then provides the user with the answer. The reasoning process and answer are enclosed within <think> </think> and <answer> </answer> tags, respectively, i.e., <think> reasoning process here </think><answer> answer here </answer>.<|im_end|>
103<|im_start|>user
104There are 25 roses in a garden. There are 40 tulips. There are 35 daisies. What percentage of flowers are not roses?.<|im_end|>
105<|im_start|>assistant
106
107
108==========
109
110
111📝 Generation:
112<think> We first need to add up the total number of flowers in the garden: 25 roses + 40 tulips + 35 daisies = 100 flowers </think> <answer> 100/25 * 100 = 40% </answer>
113
114==========
115
116
117✅ Answer:
11875
119
120==========
121
122
123🔍 Extracted Answer:
124100/25 * 100 = 40%
125
126===================================
127
128Iter 100: Val loss -1.247, Val total_rewards_mean 0.688, Val total_rewards_std 0.325, Val grouped_rewards_mean 0.688, Val grouped_rewards_std 0.325, Val kl 0.099, Val r1_accuracy_reward_func_mean 0.000, Val r1_accuracy_reward_func_std 0.000, Val r1_int_reward_func_mean 0.000, Val r1_int_reward_func_std 0.000, Val r1_strict_format_reward_func_mean 0.000, Val r1_strict_format_reward_func_std 0.000, Val r1_soft_format_reward_func_mean 0.375, Val r1_soft_format_reward_func_std 0.217, Val r1_count_xml_mean 0.312, Val r1_count_xml_std 0.108, Val took 18.438s
129Iter 100: Train loss -0.979, Total rewards mean 11.020, Total rewards std 5.108, Grouped rewards mean 11.020, Grouped rewards std 5.108, KL 0.401, r1_accuracy_reward_func mean 1.200, r1_accuracy_reward_func std 1.712, r1_int_reward_func mean 0.925, r1_int_reward_func std 1.050, r1_strict_format_reward_func mean 1.888, r1_strict_format_reward_func std 1.719, r1_soft_format_reward_func mean 4.325, r1_soft_format_reward_func std 0.918, r1_count_xml mean 2.682, r1_count_xml std 1.197, Learning Rate 1.000e-05, It/sec 0.558, Tokens/sec 301.772, Peak mem 15.609 GB
130Iter 100: Saved adapter weights to /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/adapters.safetensors and /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/0000100_adapters.safetensors.
131Iter 110: Train loss -0.781, Total rewards mean 12.089, Total rewards std 5.403, Grouped rewards mean 12.089, Grouped rewards std 5.403, KL 0.604, r1_accuracy_reward_func mean 1.200, r1_accuracy_reward_func std 1.712, r1_int_reward_func mean 0.975, r1_int_reward_func std 1.118, r1_strict_format_reward_func mean 2.162, r1_strict_format_reward_func std 1.956, r1_soft_format_reward_func mean 4.800, r1_soft_format_reward_func std 0.961, r1_count_xml mean 2.951, r1_count_xml std 1.311, Learning Rate 1.000e-05, It/sec 0.034, Tokens/sec 18.646, Peak mem 15.609 GB
132Iter 120: Train loss 0.675, Total rewards mean 13.212, Total rewards std 5.702, Grouped rewards mean 13.212, Grouped rewards std 5.702, KL 3.091, r1_accuracy_reward_func mean 1.250, r1_accuracy_reward_func std 1.799, r1_int_reward_func mean 1.013, r1_int_reward_func std 1.164, r1_strict_format_reward_func mean 2.463, r1_strict_format_reward_func std 2.149, r1_soft_format_reward_func mean 5.275, r1_soft_format_reward_func std 1.004, r1_count_xml mean 3.212, r1_count_xml std 1.431, Learning Rate 1.000e-05, It/sec 0.036, Tokens/sec 18.644, Peak mem 15.609 GB
133Iter 120: Saved adapter weights to /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/adapters.safetensors and /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/0000120_adapters.safetensors.
134Iter 130: Train loss -0.338, Total rewards mean 14.361, Total rewards std 6.298, Grouped rewards mean 14.361, Grouped rewards std 6.298, KL 3.149, r1_accuracy_reward_func mean 1.450, r1_accuracy_reward_func std 2.072, r1_int_reward_func mean 1.125, r1_int_reward_func std 1.298, r1_strict_format_reward_func mean 2.650, r1_strict_format_reward_func std 2.351, r1_soft_format_reward_func mean 5.688, r1_soft_format_reward_func std 1.094, r1_count_xml mean 3.448, r1_count_xml std 1.524, Learning Rate 1.000e-05, It/sec 0.032, Tokens/sec 18.707, Peak mem 15.609 GB
135Iter 140: Train loss 0.406, Total rewards mean 15.624, Total rewards std 6.804, Grouped rewards mean 15.624, Grouped rewards std 6.804, KL 3.218, r1_accuracy_reward_func mean 1.600, r1_accuracy_reward_func std 2.259, r1_int_reward_func mean 1.250, r1_int_reward_func std 1.434, r1_strict_format_reward_func mean 2.888, r1_strict_format_reward_func std 2.552, r1_soft_format_reward_func mean 6.137, r1_soft_format_reward_func std 1.144, r1_count_xml mean 3.749, r1_count_xml std 1.622, Learning Rate 1.000e-05, It/sec 0.038, Tokens/sec 18.650, Peak mem 15.609 GB
136Iter 140: Saved adapter weights to /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/adapters.safetensors and /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/0000140_adapters.safetensors.
137
138=== Validation Sample Details ===
139
140📋 Raw Prompt:
141There are 25 roses in a garden. There are 40 tulips. There are 35 daisies. What percentage of flowers are not roses?.
142
143==========
144
145
146🔄 Model Input:
147<|im_start|>system
148A conversation between User and Assistant. The user asks a question, and the Assistant solves it. The assistant first thinks about the reasoning process in the mind and then provides the user with the answer. The reasoning process and answer are enclosed within <think> </think> and <answer> </answer> tags, respectively, i.e., <think> reasoning process here </think><answer> answer here </answer>.<|im_end|>
149<|im_start|>user
150There are 25 roses in a garden. There are 40 tulips. There are 35 daisies. What percentage of flowers are not roses?.<|im_end|>
151<|im_start|>assistant
152
153
154==========
155
156
157📝 Generation:
158<think> We need to find the total number of flowers, subtract the number of roses from that total, and then calculate the percentage that corresponds to the remaining flowers. </think><answer> 84.29% </answer>
159
160==========
161
162
163✅ Answer:
16475
165
166==========
167
168
169🔍 Extracted Answer:
17084.29%
171
172===================================
173
174Iter 150: Val loss -1.878, Val total_rewards_mean 0.875, Val total_rewards_std 0.508, Val grouped_rewards_mean 0.875, Val grouped_rewards_std 0.508, Val kl 0.091, Val r1_accuracy_reward_func_mean 0.000, Val r1_accuracy_reward_func_std 0.000, Val r1_int_reward_func_mean 0.000, Val r1_int_reward_func_std 0.000, Val r1_strict_format_reward_func_mean 0.250, Val r1_strict_format_reward_func_std 0.250, Val r1_soft_format_reward_func_mean 0.375, Val r1_soft_format_reward_func_std 0.217, Val r1_count_xml_mean 0.250, Val r1_count_xml_std 0.153, Val took 15.434s
175Iter 150: Train loss 0.307, Total rewards mean 16.830, Total rewards std 7.143, Grouped rewards mean 16.830, Grouped rewards std 7.143, KL 3.288, r1_accuracy_reward_func mean 1.650, r1_accuracy_reward_func std 2.345, r1_int_reward_func mean 1.350, r1_int_reward_func std 1.546, r1_strict_format_reward_func mean 3.150, r1_strict_format_reward_func std 2.749, r1_soft_format_reward_func mean 6.613, r1_soft_format_reward_func std 1.169, r1_count_xml mean 4.068, r1_count_xml std 1.687, Learning Rate 1.000e-05, It/sec 0.449, Tokens/sec 219.298, Peak mem 15.609 GB
176Iter 160: Train loss -0.674, Total rewards mean 18.046, Total rewards std 7.711, Grouped rewards mean 18.046, Grouped rewards std 7.711, KL 3.355, r1_accuracy_reward_func mean 1.750, r1_accuracy_reward_func std 2.519, r1_int_reward_func mean 1.500, r1_int_reward_func std 1.701, r1_strict_format_reward_func mean 3.400, r1_strict_format_reward_func std 2.961, r1_soft_format_reward_func mean 7.037, r1_soft_format_reward_func std 1.281, r1_count_xml mean 4.358, r1_count_xml std 1.771, Learning Rate 1.000e-05, It/sec 0.035, Tokens/sec 18.453, Peak mem 15.609 GB
177Iter 160: Saved adapter weights to /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/adapters.safetensors and /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/0000160_adapters.safetensors.
178Iter 170: Train loss -0.110, Total rewards mean 19.289, Total rewards std 8.190, Grouped rewards mean 19.289, Grouped rewards std 8.190, KL 3.431, r1_accuracy_reward_func mean 1.900, r1_accuracy_reward_func std 2.705, r1_int_reward_func mean 1.575, r1_int_reward_func std 1.787, r1_strict_format_reward_func mean 3.688, r1_strict_format_reward_func std 3.187, r1_soft_format_reward_func mean 7.500, r1_soft_format_reward_func std 1.328, r1_count_xml mean 4.627, r1_count_xml std 1.889, Learning Rate 1.000e-05, It/sec 0.036, Tokens/sec 17.768, Peak mem 15.609 GB
179Iter 180: Train loss -0.537, Total rewards mean 20.361, Total rewards std 8.616, Grouped rewards mean 20.361, Grouped rewards std 8.616, KL 3.495, r1_accuracy_reward_func mean 2.000, r1_accuracy_reward_func std 2.805, r1_int_reward_func mean 1.638, r1_int_reward_func std 1.859, r1_strict_format_reward_func mean 3.900, r1_strict_format_reward_func std 3.414, r1_soft_format_reward_func mean 7.950, r1_soft_format_reward_func std 1.414, r1_count_xml mean 4.873, r1_count_xml std 2.035, Learning Rate 1.000e-05, It/sec 0.033, Tokens/sec 17.918, Peak mem 15.609 GB
180Iter 180: Saved adapter weights to /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/adapters.safetensors and /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/0000180_adapters.safetensors.
181Iter 190: Train loss -0.165, Total rewards mean 21.736, Total rewards std 9.399, Grouped rewards mean 21.736, Grouped rewards std 9.399, KL 3.570, r1_accuracy_reward_func mean 2.300, r1_accuracy_reward_func std 3.325, r1_int_reward_func mean 1.775, r1_int_reward_func std 2.061, r1_strict_format_reward_func mean 4.137, r1_strict_format_reward_func std 3.604, r1_soft_format_reward_func mean 8.413, r1_soft_format_reward_func std 1.479, r1_count_xml mean 5.111, r1_count_xml std 2.193, Learning Rate 1.000e-05, It/sec 0.034, Tokens/sec 18.594, Peak mem 15.609 GB
182
183=== Validation Sample Details ===
184
185📋 Raw Prompt:
186There are 25 roses in a garden. There are 40 tulips. There are 35 daisies. What percentage of flowers are not roses?.
187
188==========
189
190
191🔄 Model Input:
192<|im_start|>system
193A conversation between User and Assistant. The user asks a question, and the Assistant solves it. The assistant first thinks about the reasoning process in the mind and then provides the user with the answer. The reasoning process and answer are enclosed within <think> </think> and <answer> </answer> tags, respectively, i.e., <think> reasoning process here </think><answer> answer here </answer>.<|im_end|>
194<|im_start|>user
195There are 25 roses in a garden. There are 40 tulips. There are 35 daisies. What percentage of flowers are not roses?.<|im_end|>
196<|im_start|>assistant
197
198
199==========
200
201
202📝 Generation:
203<solution>
204In total, 25 + 40 + 35 = 100 flowers.
205There are 100 - 25 = 75 flowers that are not roses.
206<solution>
207<think> The total number of flowers is 25 + 40 + 35 = 100. We need to find the percentage of flowers that are not roses, which is 100 - 25 = 75. </think><answer> 75 </answer>
208
209==========
210
211
212✅ Answer:
21375
214
215==========
216
217
218🔍 Extracted Answer:
21975
220
221===================================
222
223Iter 200: Val loss 0.746, Val total_rewards_mean 1.688, Val total_rewards_std 1.267, Val grouped_rewards_mean 1.688, Val grouped_rewards_std 1.267, Val kl 0.045, Val r1_accuracy_reward_func_mean 0.500, Val r1_accuracy_reward_func_std 0.866, Val r1_int_reward_func_mean 0.125, Val r1_int_reward_func_std 0.217, Val r1_strict_format_reward_func_mean 0.250, Val r1_strict_format_reward_func_std 0.250, Val r1_soft_format_reward_func_mean 0.500, Val r1_soft_format_reward_func_std 0.000, Val r1_count_xml_mean 0.312, Val r1_count_xml_std 0.108, Val took 26.068s
224Iter 200: Train loss -0.091, Total rewards mean 22.944, Total rewards std 9.915, Grouped rewards mean 22.944, Grouped rewards std 9.915, KL 3.619, r1_accuracy_reward_func mean 2.450, r1_accuracy_reward_func std 3.585, r1_int_reward_func mean 1.850, r1_int_reward_func std 2.172, r1_strict_format_reward_func mean 4.438, r1_strict_format_reward_func std 3.834, r1_soft_format_reward_func mean 8.875, r1_soft_format_reward_func std 1.544, r1_count_xml mean 5.332, r1_count_xml std 2.360, Learning Rate 1.000e-05, It/sec 0.297, Tokens/sec 188.080, Peak mem 15.609 GB
225Iter 200: Saved adapter weights to /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/adapters.safetensors and /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/0000200_adapters.safetensors.
226Iter 210: Train loss -0.321, Total rewards mean 23.972, Total rewards std 10.221, Grouped rewards mean 23.972, Grouped rewards std 10.221, KL 3.687, r1_accuracy_reward_func mean 2.450, r1_accuracy_reward_func std 3.585, r1_int_reward_func mean 1.888, r1_int_reward_func std 2.219, r1_strict_format_reward_func mean 4.688, r1_strict_format_reward_func std 4.032, r1_soft_format_reward_func mean 9.350, r1_soft_format_reward_func std 1.587, r1_count_xml mean 5.597, r1_count_xml std 2.473, Learning Rate 1.000e-05, It/sec 0.038, Tokens/sec 18.288, Peak mem 15.609 GB
227Iter 220: Train loss -0.912, Total rewards mean 25.083, Total rewards std 10.699, Grouped rewards mean 25.083, Grouped rewards std 10.699, KL 3.755, r1_accuracy_reward_func mean 2.550, r1_accuracy_reward_func std 3.758, r1_int_reward_func mean 1.975, r1_int_reward_func std 2.309, r1_strict_format_reward_func mean 4.938, r1_strict_format_reward_func std 4.255, r1_soft_format_reward_func mean 9.800, r1_soft_format_reward_func std 1.656, r1_count_xml mean 5.820, r1_count_xml std 2.616, Learning Rate 1.000e-05, It/sec 0.033, Tokens/sec 18.307, Peak mem 15.609 GB
228Iter 220: Saved adapter weights to /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/adapters.safetensors and /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/0000220_adapters.safetensors.
229Iter 230: Train loss -1.596, Total rewards mean 26.009, Total rewards std 11.108, Grouped rewards mean 26.009, Grouped rewards std 11.108, KL 3.824, r1_accuracy_reward_func mean 2.550, r1_accuracy_reward_func std 3.758, r1_int_reward_func mean 2.037, r1_int_reward_func std 2.374, r1_strict_format_reward_func mean 5.137, r1_strict_format_reward_func std 4.467, r1_soft_format_reward_func mean 10.200, r1_soft_format_reward_func std 1.811, r1_count_xml mean 6.084, r1_count_xml std 2.756, Learning Rate 1.000e-05, It/sec 0.033, Tokens/sec 18.324, Peak mem 15.609 GB
230Iter 240: Train loss 0.491, Total rewards mean 27.309, Total rewards std 11.639, Grouped rewards mean 27.309, Grouped rewards std 11.639, KL 3.904, r1_accuracy_reward_func mean 2.800, r1_accuracy_reward_func std 4.118, r1_int_reward_func mean 2.150, r1_int_reward_func std 2.464, r1_strict_format_reward_func mean 5.338, r1_strict_format_reward_func std 4.665, r1_soft_format_reward_func mean 10.688, r1_soft_format_reward_func std 1.832, r1_count_xml mean 6.334, r1_count_xml std 2.870, Learning Rate 1.000e-05, It/sec 0.032, Tokens/sec 18.672, Peak mem 15.609 GB
231Iter 240: Saved adapter weights to /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/adapters.safetensors and /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/0000240_adapters.safetensors.
232
233=== Validation Sample Details ===
234
235📋 Raw Prompt:
236There are 25 roses in a garden. There are 40 tulips. There are 35 daisies. What percentage of flowers are not roses?.
237
238==========
239
240
241🔄 Model Input:
242<|im_start|>system
243A conversation between User and Assistant. The user asks a question, and the Assistant solves it. The assistant first thinks about the reasoning process in the mind and then provides the user with the answer. The reasoning process and answer are enclosed within <think> </think> and <answer> </answer> tags, respectively, i.e., <think> reasoning process here </think><answer> answer here </answer>.<|im_end|>
244<|im_start|>user
245There are 25 roses in a garden. There are 40 tulips. There are 35 daisies. What percentage of flowers are not roses?.<|im_end|>
246<|im_start|>assistant
247
248
249==========
250
251
252📝 Generation:
253<think> The total number of flowers in the garden is 25 + 40 + 35 = 100. The number of flowers that are not roses is 40 + 35 = 75. The percentage of flowers that are not roses is 75/100 * 100 = 75% </think><answer> 75% </answer>
254
255==========
256
257
258✅ Answer:
25975
260
261==========
262
263
264🔍 Extracted Answer:
26575%
266
267===================================
268
269Iter 250: Val loss -2.211, Val total_rewards_mean 1.250, Val total_rewards_std 0.217, Val grouped_rewards_mean 1.250, Val grouped_rewards_std 0.217, Val kl 0.097, Val r1_accuracy_reward_func_mean 0.000, Val r1_accuracy_reward_func_std 0.000, Val r1_int_reward_func_mean 0.000, Val r1_int_reward_func_std 0.000, Val r1_strict_format_reward_func_mean 0.375, Val r1_strict_format_reward_func_std 0.217, Val r1_soft_format_reward_func_mean 0.500, Val r1_soft_format_reward_func_std 0.000, Val r1_count_xml_mean 0.375, Val r1_count_xml_std 0.000, Val took 15.147s
270Iter 250: Train loss -1.791, Total rewards mean 28.546, Total rewards std 12.248, Grouped rewards mean 28.546, Grouped rewards std 12.248, KL 3.968, r1_accuracy_reward_func mean 2.950, r1_accuracy_reward_func std 4.377, r1_int_reward_func mean 2.237, r1_int_reward_func std 2.597, r1_strict_format_reward_func mean 5.588, r1_strict_format_reward_func std 4.895, r1_soft_format_reward_func mean 11.163, r1_soft_format_reward_func std 1.876, r1_count_xml mean 6.608, r1_count_xml std 2.981, Learning Rate 1.000e-05, It/sec 0.293, Tokens/sec 156.096, Peak mem 15.609 GB
271Iter 260: Train loss -0.323, Total rewards mean 30.033, Total rewards std 13.043, Grouped rewards mean 30.033, Grouped rewards std 13.043, KL 4.021, r1_accuracy_reward_func mean 3.300, r1_accuracy_reward_func std 4.984, r1_int_reward_func mean 2.463, r1_int_reward_func std 2.752, r1_strict_format_reward_func mean 5.800, r1_strict_format_reward_func std 5.115, r1_soft_format_reward_func mean 11.613, r1_soft_format_reward_func std 1.944, r1_count_xml mean 6.858, r1_count_xml std 3.125, Learning Rate 1.000e-05, It/sec 0.027, Tokens/sec 18.002, Peak mem 19.129 GB
272Iter 260: Saved adapter weights to /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/adapters.safetensors and /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/0000260_adapters.safetensors.
273Iter 270: Train loss 0.025, Total rewards mean 31.266, Total rewards std 13.536, Grouped rewards mean 31.266, Grouped rewards std 13.536, KL 4.082, r1_accuracy_reward_func mean 3.400, r1_accuracy_reward_func std 5.157, r1_int_reward_func mean 2.537, r1_int_reward_func std 2.845, r1_strict_format_reward_func mean 6.137, r1_strict_format_reward_func std 5.273, r1_soft_format_reward_func mean 12.075, r1_soft_format_reward_func std 2.009, r1_count_xml mean 7.116, r1_count_xml std 3.244, Learning Rate 1.000e-05, It/sec 0.034, Tokens/sec 18.453, Peak mem 19.129 GB
274Iter 280: Train loss -2.500, Total rewards mean 32.471, Total rewards std 13.946, Grouped rewards mean 32.471, Grouped rewards std 13.946, KL 4.177, r1_accuracy_reward_func mean 3.500, r1_accuracy_reward_func std 5.330, r1_int_reward_func mean 2.600, r1_int_reward_func std 2.935, r1_strict_format_reward_func mean 6.400, r1_strict_format_reward_func std 5.481, r1_soft_format_reward_func mean 12.538, r1_soft_format_reward_func std 2.055, r1_count_xml mean 7.434, r1_count_xml std 3.317, Learning Rate 1.000e-05, It/sec 0.039, Tokens/sec 18.447, Peak mem 19.129 GB
275Iter 280: Saved adapter weights to /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/adapters.safetensors and /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/0000280_adapters.safetensors.
276Iter 290: Train loss 0.255, Total rewards mean 33.556, Total rewards std 14.219, Grouped rewards mean 33.556, Grouped rewards std 14.219, KL 4.265, r1_accuracy_reward_func mean 3.500, r1_accuracy_reward_func std 5.330, r1_int_reward_func mean 2.625, r1_int_reward_func std 2.978, r1_strict_format_reward_func mean 6.688, r1_strict_format_reward_func std 5.683, r1_soft_format_reward_func mean 13.025, r1_soft_format_reward_func std 2.077, r1_count_xml mean 7.718, r1_count_xml std 3.432, Learning Rate 1.000e-05, It/sec 0.038, Tokens/sec 18.379, Peak mem 19.129 GB
277
278=== Validation Sample Details ===
279
280📋 Raw Prompt:
281There are 25 roses in a garden. There are 40 tulips. There are 35 daisies. What percentage of flowers are not roses?.
282
283==========
284
285
286🔄 Model Input:
287<|im_start|>system
288A conversation between User and Assistant. The user asks a question, and the Assistant solves it. The assistant first thinks about the reasoning process in the mind and then provides the user with the answer. The reasoning process and answer are enclosed within <think> </think> and <answer> </answer> tags, respectively, i.e., <think> reasoning process here </think><answer> answer here </answer>.<|im_end|>
289<|im_start|>user
290There are 25 roses in a garden. There are 40 tulips. There are 35 daisies. What percentage of flowers are not roses?.<|im_end|>
291<|im_start|>assistant
292
293
294==========
295
296
297📝 Generation:
298<think> We can think about the percentage here as a fraction. There are a total of 60 flowers. We are interested in the percentage of other flowers. 60 - 25 = 35 <answer> 58.33% </answer>
299
300==========
301
302
303✅ Answer:
30475
305
306==========
307
308
309🔍 Extracted Answer:
31058.33%
311
312===================================
313
314Iter 300: Val loss -1.223, Val total_rewards_mean 0.688, Val total_rewards_std 0.325, Val grouped_rewards_mean 0.688, Val grouped_rewards_std 0.325, Val kl 0.058, Val r1_accuracy_reward_func_mean 0.000, Val r1_accuracy_reward_func_std 0.000, Val r1_int_reward_func_mean 0.000, Val r1_int_reward_func_std 0.000, Val r1_strict_format_reward_func_mean 0.250, Val r1_strict_format_reward_func_std 0.250, Val r1_soft_format_reward_func_mean 0.375, Val r1_soft_format_reward_func_std 0.217, Val r1_count_xml_mean 0.062, Val r1_count_xml_std 0.108, Val took 20.780s
315Iter 300: Train loss 0.123, Total rewards mean 34.595, Total rewards std 14.587, Grouped rewards mean 34.595, Grouped rewards std 14.587, KL 4.339, r1_accuracy_reward_func mean 3.550, r1_accuracy_reward_func std 5.417, r1_int_reward_func mean 2.662, r1_int_reward_func std 3.043, r1_strict_format_reward_func mean 6.912, r1_strict_format_reward_func std 5.894, r1_soft_format_reward_func mean 13.500, r1_soft_format_reward_func std 2.120, r1_count_xml mean 7.970, r1_count_xml std 3.569, Learning Rate 1.000e-05, It/sec 0.446, Tokens/sec 240.707, Peak mem 19.129 GB
316Iter 300: Saved adapter weights to /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/adapters.safetensors and /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/0000300_adapters.safetensors.
317Iter 310: Train loss -1.306, Total rewards mean 35.881, Total rewards std 15.126, Grouped rewards mean 35.881, Grouped rewards std 15.126, KL 4.431, r1_accuracy_reward_func mean 3.700, r1_accuracy_reward_func std 5.677, r1_int_reward_func mean 2.737, r1_int_reward_func std 3.130, r1_strict_format_reward_func mean 7.275, r1_strict_format_reward_func std 6.028, r1_soft_format_reward_func mean 13.938, r1_soft_format_reward_func std 2.229, r1_count_xml mean 8.231, r1_count_xml std 3.686, Learning Rate 1.000e-05, It/sec 0.036, Tokens/sec 18.488, Peak mem 19.129 GB
318Iter 320: Train loss -2.792, Total rewards mean 36.951, Total rewards std 15.523, Grouped rewards mean 36.951, Grouped rewards std 15.523, KL 4.533, r1_accuracy_reward_func mean 3.750, r1_accuracy_reward_func std 5.763, r1_int_reward_func mean 2.812, r1_int_reward_func std 3.242, r1_strict_format_reward_func mean 7.537, r1_strict_format_reward_func std 6.192, r1_soft_format_reward_func mean 14.363, r1_soft_format_reward_func std 2.340, r1_count_xml mean 8.489, r1_count_xml std 3.838, Learning Rate 1.000e-05, It/sec 0.041, Tokens/sec 18.199, Peak mem 19.129 GB
319Iter 320: Saved adapter weights to /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/adapters.safetensors and /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/0000320_adapters.safetensors.
320Iter 330: Train loss 0.071, Total rewards mean 38.183, Total rewards std 16.066, Grouped rewards mean 38.183, Grouped rewards std 16.066, KL 4.598, r1_accuracy_reward_func mean 3.900, r1_accuracy_reward_func std 6.023, r1_int_reward_func mean 2.912, r1_int_reward_func std 3.397, r1_strict_format_reward_func mean 7.750, r1_strict_format_reward_func std 6.394, r1_soft_format_reward_func mean 14.850, r1_soft_format_reward_func std 2.362, r1_count_xml mean 8.770, r1_count_xml std 3.948, Learning Rate 1.000e-05, It/sec 0.033, Tokens/sec 18.575, Peak mem 19.129 GB
321Iter 340: Train loss -0.522, Total rewards mean 39.573, Total rewards std 16.618, Grouped rewards mean 39.573, Grouped rewards std 16.618, KL 4.700, r1_accuracy_reward_func mean 4.150, r1_accuracy_reward_func std 6.310, r1_int_reward_func mean 3.013, r1_int_reward_func std 3.533, r1_strict_format_reward_func mean 8.038, r1_strict_format_reward_func std 6.584, r1_soft_format_reward_func mean 15.337, r1_soft_format_reward_func std 2.384, r1_count_xml mean 9.036, r1_count_xml std 4.027, Learning Rate 1.000e-05, It/sec 0.039, Tokens/sec 18.497, Peak mem 19.129 GB
322Iter 340: Saved adapter weights to /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/adapters.safetensors and /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/0000340_adapters.safetensors.
323
324=== Validation Sample Details ===
325
326📋 Raw Prompt:
327There are 25 roses in a garden. There are 40 tulips. There are 35 daisies. What percentage of flowers are not roses?.
328
329==========
330
331
332🔄 Model Input:
333<|im_start|>system
334A conversation between User and Assistant. The user asks a question, and the Assistant solves it. The assistant first thinks about the reasoning process in the mind and then provides the user with the answer. The reasoning process and answer are enclosed within <think> </think> and <answer> </answer> tags, respectively, i.e., <think> reasoning process here </think><answer> answer here </answer>.<|im_end|>
335<|im_start|>user
336There are 25 roses in a garden. There are 40 tulips. There are 35 daisies. What percentage of flowers are not roses?.<|im_end|>
337<|im_start|>assistant
338
339
340==========
341
342
343📝 Generation:
344<think> To find out what percentage of flowers are not roses, we need to follow these steps: </think>
345<answer> Calculate the total number of flowers in the garden. Subtract the number of roses from the total number of flowers to find the number of flowers that are neither roses nor tulips or daisies. Then, find the percentage of flowers that are not roses by dividing the number of flowers that are not roses by the total number of flowers and multiplying by 100. </answer>
346
347==========
348
349
350✅ Answer:
35175
352
353==========
354
355
356🔍 Extracted Answer:
357Calculate the total number of flowers in the garden. Subtract the number of roses from the total number of flowers to find the number of flowers that are neither roses nor tulips or daisies. Then, find the percentage of flowers that are not roses by dividing the number of flowers that are not roses by the total number of flowers and multiplying by 100.
358
359===================================
360
361Iter 350: Val loss -2.649, Val total_rewards_mean 1.156, Val total_rewards_std 0.223, Val grouped_rewards_mean 1.156, Val grouped_rewards_std 0.223, Val kl 0.131, Val r1_accuracy_reward_func_mean 0.000, Val r1_accuracy_reward_func_std 0.000, Val r1_int_reward_func_mean 0.000, Val r1_int_reward_func_std 0.000, Val r1_strict_format_reward_func_mean 0.375, Val r1_strict_format_reward_func_std 0.217, Val r1_soft_format_reward_func_mean 0.500, Val r1_soft_format_reward_func_std 0.000, Val r1_count_xml_mean 0.281, Val r1_count_xml_std 0.162, Val took 14.459s
362Iter 350: Train loss -0.594, Total rewards mean 40.711, Total rewards std 17.084, Grouped rewards mean 40.711, Grouped rewards std 17.084, KL 4.775, r1_accuracy_reward_func mean 4.250, r1_accuracy_reward_func std 6.483, r1_int_reward_func mean 3.075, r1_int_reward_func std 3.623, r1_strict_format_reward_func mean 8.275, r1_strict_format_reward_func std 6.786, r1_soft_format_reward_func mean 15.800, r1_soft_format_reward_func std 2.449, r1_count_xml mean 9.311, r1_count_xml std 4.137, Learning Rate 1.000e-05, It/sec 0.326, Tokens/sec 187.077, Peak mem 19.129 GB
363Iter 360: Train loss -0.318, Total rewards mean 41.833, Total rewards std 17.455, Grouped rewards mean 41.833, Grouped rewards std 17.455, KL 4.857, r1_accuracy_reward_func mean 4.300, r1_accuracy_reward_func std 6.569, r1_int_reward_func mean 3.162, r1_int_reward_func std 3.695, r1_strict_format_reward_func mean 8.525, r1_strict_format_reward_func std 6.959, r1_soft_format_reward_func mean 16.250, r1_soft_format_reward_func std 2.535, r1_count_xml mean 9.596, r1_count_xml std 4.234, Learning Rate 1.000e-05, It/sec 0.037, Tokens/sec 18.497, Peak mem 19.129 GB
364Iter 360: Saved adapter weights to /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/adapters.safetensors and /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/0000360_adapters.safetensors.
365Iter 370: Train loss 0.467, Total rewards mean 43.073, Total rewards std 17.890, Grouped rewards mean 43.073, Grouped rewards std 17.890, KL 4.972, r1_accuracy_reward_func mean 4.450, r1_accuracy_reward_func std 6.756, r1_int_reward_func mean 3.237, r1_int_reward_func std 3.770, r1_strict_format_reward_func mean 8.812, r1_strict_format_reward_func std 7.149, r1_soft_format_reward_func mean 16.725, r1_soft_format_reward_func std 2.578, r1_count_xml mean 9.848, r1_count_xml std 4.373, Learning Rate 1.000e-05, It/sec 0.040, Tokens/sec 18.342, Peak mem 19.129 GB
366Iter 380: Train loss -0.234, Total rewards mean 44.282, Total rewards std 18.515, Grouped rewards mean 44.282, Grouped rewards std 18.515, KL 5.046, r1_accuracy_reward_func mean 4.600, r1_accuracy_reward_func std 7.016, r1_int_reward_func mean 3.287, r1_int_reward_func std 3.856, r1_strict_format_reward_func mean 9.075, r1_strict_format_reward_func std 7.375, r1_soft_format_reward_func mean 17.188, r1_soft_format_reward_func std 2.643, r1_count_xml mean 10.132, r1_count_xml std 4.491, Learning Rate 1.000e-05, It/sec 0.035, Tokens/sec 18.543, Peak mem 19.129 GB
367Iter 380: Saved adapter weights to /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/adapters.safetensors and /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/0000380_adapters.safetensors.
368Iter 390: Train loss -0.686, Total rewards mean 45.690, Total rewards std 19.125, Grouped rewards mean 45.690, Grouped rewards std 19.125, KL 5.122, r1_accuracy_reward_func mean 4.900, r1_accuracy_reward_func std 7.289, r1_int_reward_func mean 3.425, r1_int_reward_func std 3.996, r1_strict_format_reward_func mean 9.375, r1_strict_format_reward_func std 7.580, r1_soft_format_reward_func mean 17.663, r1_soft_format_reward_func std 2.687, r1_count_xml mean 10.328, r1_count_xml std 4.636, Learning Rate 1.000e-05, It/sec 0.033, Tokens/sec 18.480, Peak mem 19.129 GB
369
370=== Validation Sample Details ===
371
372📋 Raw Prompt:
373There are 25 roses in a garden. There are 40 tulips. There are 35 daisies. What percentage of flowers are not roses?.
374
375==========
376
377
378🔄 Model Input:
379<|im_start|>system
380A conversation between User and Assistant. The user asks a question, and the Assistant solves it. The assistant first thinks about the reasoning process in the mind and then provides the user with the answer. The reasoning process and answer are enclosed within <think> </think> and <answer> </answer> tags, respectively, i.e., <think> reasoning process here </think><answer> answer here </answer>.<|im_end|>
381<|im_start|>user
382There are 25 roses in a garden. There are 40 tulips. There are 35 daisies. What percentage of flowers are not roses?.<|im_end|>
383<|im_start|>assistant
384
385
386==========
387
388
389📝 Generation:
390<think> Calculate the total number of flowers </think><answer> 25 + 40 + 35 = 100 </answer> <think> Calculate the number of flowers that are not roses </think><answer> 100 - 25 = 75 </answer> <think> Calculate the percentage of flowers that are not roses </think><answer> 75 / 100 = 75% </answer> 75% of the flowers are not roses.
391
392==========
393
394
395✅ Answer:
39675
397
398==========
399
400
401🔍 Extracted Answer:
40275 / 100 = 75%
403
404===================================
405
406Iter 400: Val loss -3.355, Val total_rewards_mean 1.281, Val total_rewards_std 0.162, Val grouped_rewards_mean 1.281, Val grouped_rewards_std 0.162, Val kl 0.149, Val r1_accuracy_reward_func_mean 0.000, Val r1_accuracy_reward_func_std 0.000, Val r1_int_reward_func_mean 0.000, Val r1_int_reward_func_std 0.000, Val r1_strict_format_reward_func_mean 0.500, Val r1_strict_format_reward_func_std 0.000, Val r1_soft_format_reward_func_mean 0.500, Val r1_soft_format_reward_func_std 0.000, Val r1_count_xml_mean 0.281, Val r1_count_xml_std 0.162, Val took 17.742s
407Iter 400: Train loss -0.257, Total rewards mean 46.887, Total rewards std 19.556, Grouped rewards mean 46.887, Grouped rewards std 19.556, KL 5.216, r1_accuracy_reward_func mean 5.000, r1_accuracy_reward_func std 7.462, r1_int_reward_func mean 3.500, r1_int_reward_func std 4.065, r1_strict_format_reward_func mean 9.675, r1_strict_format_reward_func std 7.742, r1_soft_format_reward_func mean 18.112, r1_soft_format_reward_func std 2.755, r1_count_xml mean 10.599, r1_count_xml std 4.770, Learning Rate 1.000e-05, It/sec 0.559, Tokens/sec 286.081, Peak mem 19.129 GB
408Iter 400: Saved adapter weights to /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/adapters.safetensors and /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/0000400_adapters.safetensors.
409Iter 410: Train loss -0.331, Total rewards mean 48.110, Total rewards std 20.060, Grouped rewards mean 48.110, Grouped rewards std 20.060, KL 5.354, r1_accuracy_reward_func mean 5.150, r1_accuracy_reward_func std 7.722, r1_int_reward_func mean 3.612, r1_int_reward_func std 4.198, r1_strict_format_reward_func mean 9.950, r1_strict_format_reward_func std 7.953, r1_soft_format_reward_func mean 18.550, r1_soft_format_reward_func std 2.863, r1_count_xml mean 10.848, r1_count_xml std 4.897, Learning Rate 1.000e-05, It/sec 0.030, Tokens/sec 18.754, Peak mem 19.129 GB
410Iter 420: Train loss -0.031, Total rewards mean 49.229, Total rewards std 20.523, Grouped rewards mean 49.229, Grouped rewards std 20.523, KL 5.439, r1_accuracy_reward_func mean 5.200, r1_accuracy_reward_func std 7.809, r1_int_reward_func mean 3.688, r1_int_reward_func std 4.266, r1_strict_format_reward_func mean 10.212, r1_strict_format_reward_func std 8.187, r1_soft_format_reward_func mean 18.975, r1_soft_format_reward_func std 2.975, r1_count_xml mean 11.154, r1_count_xml std 4.997, Learning Rate 1.000e-05, It/sec 0.034, Tokens/sec 18.377, Peak mem 19.129 GB
411Iter 420: Saved adapter weights to /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/adapters.safetensors and /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/0000420_adapters.safetensors.
412Iter 430: Train loss 20.228, Total rewards mean 50.436, Total rewards std 21.019, Grouped rewards mean 50.436, Grouped rewards std 21.019, KL 210.900, r1_accuracy_reward_func mean 5.300, r1_accuracy_reward_func std 7.982, r1_int_reward_func mean 3.800, r1_int_reward_func std 4.388, r1_strict_format_reward_func mean 10.462, r1_strict_format_reward_func std 8.391, r1_soft_format_reward_func mean 19.438, r1_soft_format_reward_func std 3.040, r1_count_xml mean 11.436, r1_count_xml std 5.111, Learning Rate 1.000e-05, It/sec 0.041, Tokens/sec 18.370, Peak mem 19.129 GB
413Iter 440: Train loss -0.072, Total rewards mean 51.886, Total rewards std 21.842, Grouped rewards mean 51.886, Grouped rewards std 21.842, KL 210.971, r1_accuracy_reward_func mean 5.700, r1_accuracy_reward_func std 8.528, r1_int_reward_func mean 3.900, r1_int_reward_func std 4.524, r1_strict_format_reward_func mean 10.700, r1_strict_format_reward_func std 8.525, r1_soft_format_reward_func mean 19.900, r1_soft_format_reward_func std 3.105, r1_count_xml mean 11.686, r1_count_xml std 5.280, Learning Rate 1.000e-05, It/sec 0.036, Tokens/sec 18.497, Peak mem 19.129 GB
414Iter 440: Saved adapter weights to /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/adapters.safetensors and /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/0000440_adapters.safetensors.
415
416=== Validation Sample Details ===
417
418📋 Raw Prompt:
419There are 25 roses in a garden. There are 40 tulips. There are 35 daisies. What percentage of flowers are not roses?.
420
421==========
422
423
424🔄 Model Input:
425<|im_start|>system
426A conversation between User and Assistant. The user asks a question, and the Assistant solves it. The assistant first thinks about the reasoning process in the mind and then provides the user with the answer. The reasoning process and answer are enclosed within <think> </think> and <answer> </answer> tags, respectively, i.e., <think> reasoning process here </think><answer> answer here </answer>.<|im_end|>
427<|im_start|>user
428There are 25 roses in a garden. There are 40 tulips. There are 35 daisies. What percentage of flowers are not roses?.<|im_end|>
429<|im_start|>assistant
430
431
432==========
433
434
435📝 Generation:
436<think> calculating the total number of flowers </think><answer> 25 + 40 + 35 = 100 </answer><think> Calculating the number of non-rose flowers </think><answer> 40 + 35 = 75 </answer><think> Calculating percentage of non-rose flowers </think><answer> (75 / 100) * 100 = 75% </answer>
437
438==========
439
440
441✅ Answer:
44275
443
444==========
445
446
447🔍 Extracted Answer:
448(75 / 100) * 100 = 75%
449
450===================================
451
452Iter 450: Val loss -0.342, Val total_rewards_mean 1.156, Val total_rewards_std 0.223, Val grouped_rewards_mean 1.156, Val grouped_rewards_std 0.223, Val kl 0.112, Val r1_accuracy_reward_func_mean 0.000, Val r1_accuracy_reward_func_std 0.000, Val r1_int_reward_func_mean 0.000, Val r1_int_reward_func_std 0.000, Val r1_strict_format_reward_func_mean 0.375, Val r1_strict_format_reward_func_std 0.217, Val r1_soft_format_reward_func_mean 0.500, Val r1_soft_format_reward_func_std 0.000, Val r1_count_xml_mean 0.281, Val r1_count_xml_std 0.162, Val took 15.687s
453Iter 450: Train loss -0.592, Total rewards mean 52.918, Total rewards std 22.259, Grouped rewards mean 52.918, Grouped rewards std 22.259, KL 211.036, r1_accuracy_reward_func mean 5.750, r1_accuracy_reward_func std 8.615, r1_int_reward_func mean 3.987, r1_int_reward_func std 4.621, r1_strict_format_reward_func mean 10.925, r1_strict_format_reward_func std 8.761, r1_soft_format_reward_func mean 20.350, r1_soft_format_reward_func std 3.173, r1_count_xml mean 11.905, r1_count_xml std 5.424, Learning Rate 1.000e-05, It/sec 0.307, Tokens/sec 202.405, Peak mem 19.129 GB
454Iter 460: Train loss -0.369, Total rewards mean 54.165, Total rewards std 22.714, Grouped rewards mean 54.165, Grouped rewards std 22.714, KL 211.112, r1_accuracy_reward_func mean 5.900, r1_accuracy_reward_func std 8.875, r1_int_reward_func mean 4.062, r1_int_reward_func std 4.696, r1_strict_format_reward_func mean 11.225, r1_strict_format_reward_func std 8.941, r1_soft_format_reward_func mean 20.825, r1_soft_format_reward_func std 3.216, r1_count_xml mean 12.152, r1_count_xml std 5.502, Learning Rate 1.000e-05, It/sec 0.033, Tokens/sec 18.610, Peak mem 19.129 GB
455Iter 460: Saved adapter weights to /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/adapters.safetensors and /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/0000460_adapters.safetensors.
456Iter 470: Train loss -0.346, Total rewards mean 55.242, Total rewards std 23.097, Grouped rewards mean 55.242, Grouped rewards std 23.097, KL 211.168, r1_accuracy_reward_func mean 5.900, r1_accuracy_reward_func std 8.875, r1_int_reward_func mean 4.137, r1_int_reward_func std 4.808, r1_strict_format_reward_func mean 11.512, r1_strict_format_reward_func std 9.125, r1_soft_format_reward_func mean 21.275, r1_soft_format_reward_func std 3.303, r1_count_xml mean 12.417, r1_count_xml std 5.624, Learning Rate 1.000e-05, It/sec 0.027, Tokens/sec 18.566, Peak mem 19.129 GB
457Iter 480: Train loss -0.784, Total rewards mean 56.456, Total rewards std 23.559, Grouped rewards mean 56.456, Grouped rewards std 23.559, KL 211.253, r1_accuracy_reward_func mean 6.000, r1_accuracy_reward_func std 9.048, r1_int_reward_func mean 4.238, r1_int_reward_func std 4.908, r1_strict_format_reward_func mean 11.738, r1_strict_format_reward_func std 9.329, r1_soft_format_reward_func mean 21.763, r1_soft_format_reward_func std 3.325, r1_count_xml mean 12.718, r1_count_xml std 5.714, Learning Rate 1.000e-05, It/sec 0.041, Tokens/sec 18.347, Peak mem 19.129 GB
458Iter 480: Saved adapter weights to /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/adapters.safetensors and /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/0000480_adapters.safetensors.
459Iter 490: Train loss -0.775, Total rewards mean 57.515, Total rewards std 23.916, Grouped rewards mean 57.515, Grouped rewards std 23.916, KL 211.321, r1_accuracy_reward_func mean 6.050, r1_accuracy_reward_func std 9.134, r1_int_reward_func mean 4.300, r1_int_reward_func std 4.998, r1_strict_format_reward_func mean 12.012, r1_strict_format_reward_func std 9.491, r1_soft_format_reward_func mean 22.225, r1_soft_format_reward_func std 3.390, r1_count_xml mean 12.928, r1_count_xml std 5.847, Learning Rate 1.000e-05, It/sec 0.029, Tokens/sec 18.415, Peak mem 19.129 GB
460
461=== Validation Sample Details ===
462
463📋 Raw Prompt:
464There are 25 roses in a garden. There are 40 tulips. There are 35 daisies. What percentage of flowers are not roses?.
465
466==========
467
468
469🔄 Model Input:
470<|im_start|>system
471A conversation between User and Assistant. The user asks a question, and the Assistant solves it. The assistant first thinks about the reasoning process in the mind and then provides the user with the answer. The reasoning process and answer are enclosed within <think> </think> and <answer> </answer> tags, respectively, i.e., <think> reasoning process here </think><answer> answer here </answer>.<|im_end|>
472<|im_start|>user
473There are 25 roses in a garden. There are 40 tulips. There are 35 daisies. What percentage of flowers are not roses?.<|im_end|>
474<|im_start|>assistant
475
476
477==========
478
479
480📝 Generation:
481<think> We can calculate the total number of plants in the garden by adding the number of each type of plant together: </think><answer>total plants=25+40+35=100 </answer><think> Number of non-rose plants is 100-25=75 </think><answer>Number of non-rose plants is 100-25=75 </answer><think> The percentage of not-rose flowers is 75/100*100=75% </think><answer>75% </answer>
482
483==========
484
485
486✅ Answer:
48775
488
489==========
490
491
492🔍 Extracted Answer:
49375%
494
495===================================
496
497Iter 500: Val loss -0.600, Val total_rewards_mean 1.094, Val total_rewards_std 0.495, Val grouped_rewards_mean 1.094, Val grouped_rewards_std 0.495, Val kl 0.091, Val r1_accuracy_reward_func_mean 0.000, Val r1_accuracy_reward_func_std 0.000, Val r1_int_reward_func_mean 0.125, Val r1_int_reward_func_std 0.217, Val r1_strict_format_reward_func_mean 0.375, Val r1_strict_format_reward_func_std 0.217, Val r1_soft_format_reward_func_mean 0.500, Val r1_soft_format_reward_func_std 0.000, Val r1_count_xml_mean 0.094, Val r1_count_xml_std 0.162, Val took 17.482s
498Iter 500: Train loss -1.223, Total rewards mean 58.690, Total rewards std 24.391, Grouped rewards mean 58.690, Grouped rewards std 24.391, KL 211.407, r1_accuracy_reward_func mean 6.150, r1_accuracy_reward_func std 9.308, r1_int_reward_func mean 4.387, r1_int_reward_func std 5.113, r1_strict_format_reward_func mean 12.238, r1_strict_format_reward_func std 9.728, r1_soft_format_reward_func mean 22.688, r1_soft_format_reward_func std 3.436, r1_count_xml mean 13.228, r1_count_xml std 5.943, Learning Rate 1.000e-05, It/sec 0.499, Tokens/sec 257.881, Peak mem 19.129 GB
499Iter 500: Saved adapter weights to /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/adapters.safetensors and /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/0000500_adapters.safetensors.
500Saved final weights to /Users/gokdenizgulmez/Library/Mobile Documents/com~apple~CloudDocs/Datastes/MLX/Josie-r1-mini/adapters.safetensors.
501Testing
502Fetching 7 files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 46382.51it/s]
503
504=== Validation Sample Details ===
505
506📋 Raw Prompt:
507Arnel had ten boxes of pencils with the same number of pencils in each box. He kept ten pencils and shared the remaining pencils equally with his five friends. If his friends got eight pencils each, how many pencils are in each box?.
508
509==========
510
511
512🔄 Model Input:
513<|im_start|>system
514A conversation between User and Assistant. The user asks a question, and the Assistant solves it. The assistant first thinks about the reasoning process in the mind and then provides the user with the answer. The reasoning process and answer are enclosed within <think> </think> and <answer> </answer> tags, respectively, i.e., <think> reasoning process here </think><answer> answer here </answer>.<|im_end|>
515<|im_start|>user
516Arnel had ten boxes of pencils with the same number of pencils in each box. He kept ten pencils and shared the remaining pencils equally with his five friends. If his friends got eight pencils each, how many pencils are in each box?.<|im_end|>
517<|im_start|>assistant
518
519
520==========
521
522
523📝 Generation:
524<think> Let's denote the number of pencils in each box as \( x \). Arnel has \( 10x \) pencils in total, and he kept 10 pencils for himself, leaving him with \( 10x - 10 \) pencils to share. Since he has 5 friends, and each friend gets 8 pencils, the total number of pencils shared among his friends is \( 5 \times 8 = 40 \). Therefore, we can write the equation: \[ 10x - 10 = 40 \] \[ 10x = 40 + 10 \] \[ 10x = 50 \] \[ x = 50 / 10 \] \[ x = 5 \] </think><answer>5</answer>
525
526==========
527
528
529✅ Answer:
5305
531
532==========
533
534
535🔍 Extracted Answer:
5365
537
538===================================
539
540Test loss -0.147, Test ppl 0.863, Rewards: total_rewards_mean: 2.438, total_rewards_std: 1.204, grouped_rewards_mean: 2.438, grouped_rewards_std: 1.204, kl: 0.051, r1_accuracy_reward_func_mean: 1.000, r1_accuracy_reward_func_std: 1.000, r1_int_reward_func_mean: 0.375, r1_int_reward_func_std: 0.217, r1_strict_format_reward_func_mean: 0.250, r1_strict_format_reward_func_std: 0.250, r1_soft_format_reward_func_mean: 0.500, r1_soft_format_reward_func_std: 0.000, r1_count_xml_mean: 0.312, r1_count_xml_std: 0.108