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Qwen/Qwen2.5-3B-Instruct 的推理能力。完整 merged 模型:steven0226/qwen2.5-3b-grpo-gsm8k。
訓練程式與獎勵函數:GitHub — grpo-rlvr-reasoning。| 獎勵函數 | 條件 | 分數 |
|---|---|---|
| correctness_reward | <answer> 內數字 == 標準答案 | 2.0 |
| strict_format_reward | 完整 <reasoning>...</reasoning><answer>...</answer> 結構 | 0.5 |
| soft_format_reward | 兩組 tag 依序出現(部分符合) | 0.5 |
| number_only_reward | <answer> 是純數字 | 0.5 |


<reasoning>
To find out how many clips Natalia sold altogether in April and May, we need to follow these steps:
1. Determine the number of clips sold in May.
2. Sum the clips sold in April and May.
First, we know that Natalia sold 48 clips in April. According to the problem, she sold half as many clips in May as she did in April. Thus, the number of clips sold in May would be:
\[
\text{Clips sold in May} = \frac{48}{2} = 24
\]
Next, to find the total number of clips sold in April and May, we add the clips sold in each month:
\[
\text{Total clips sold} = \text{Clips sold in April} + \text{Clips sold in May} = 48 + 24
\]
Performing the addition:
\[
48 + 24 = 72
\]
Therefore, Natalia sold a total of 72 clips in April and May.
</reasoning>
<answer>
72 clips
Natalia sold a total of 72 clips in April and May. </answer><reasoning>
To determine the total number of clips Natalia sold in April and May, we first need to find out how many clips she sold in May. According to the problem, Natalia sold half as many clips in May as she did in April. Since she sold 48 clips in April, the number of clips sold in May is:
\[ \text{Clips sold in May} = \frac{48}{2} = 24 \]
Next, we need to find the total number of clips sold in both April and May by adding the clips sold in April to the clips sold in May:
\[ \text{Total clips sold} = \text{Clips sold in April} + \text{Clips sold in May} = 48 + 24 = 72 \]
Therefore, the total number of clips Natalia sold in April and May is 72.
</reasoning>
<answer>
72
</answer><reasoning>
To determine how much Weng earned, we first need to convert her babysitting time into hours since her hourly rate is given in dollars per hour. Since there are 60 minutes in an hour, 50 minutes can be converted to hours by dividing 50 by 60.
Next, we multiply the number of hours she worked by her hourly wage of $12 to find out how much she earned.
\[
\text{Hours worked} = \frac{50}{60} = \frac{5}{6} \text{ hours}
\]
\[
\text{Earnings} = \text{Hours worked} \times \text{Hourly wage} = \frac{5}{6} \times 12
\]
\[
\text{Earnings} = \frac{5 \times 12}{6} = \frac{60}{6} = 10
\]
Therefore, Weng earned $10.
</reasoning>
<answer>
Weng earned $10 for babysitting 50 minutes.
</answer><reasoning>
To determine how much Weng earned, we need to calculate the earnings based on the hourly rate and the number of hours worked. First, we need to convert the time worked from minutes to hours. We know that 1 hour is equal to 60 minutes. Weng worked for 50 minutes, which is less than 1 hour. We can find out how many hours 50 minutes is equivalent to by using the conversion factor:
\[ \text{Hours} = \frac{\text{Minutes}}{60} \]
Substituting 50 for minutes:
\[ \text{Hours} = \frac{50}{60} = \frac{5}{6} \text{ hours} \]
Next, we calculate the earnings by multiplying the hourly rate by the number of hours worked:
\[ \text{Earnings} = \text{Hourly Rate} \times \text{Hours Worked} \]
Substituting $12 for the hourly rate and \(\frac{5}{6}\) for the hours worked:
\[ \text{Earnings} = 12 \times \frac{5}{6} \]
To simplify the multiplication:
\[ \text{Earnings} = 12 \times \frac{5}{6} = 12 \times \frac{5}{6} = 2 \times 5 = 10 \]
Therefore, Weng earned $10.
</reasoning>
<answer>
10
</answer>| 項目 | 值 |
|---|---|
| base model | Qwen/Qwen2.5-3B-Instruct |
| 演算法 | GRPO(TRL + Unsloth,vLLM rollout) |
| LoRA rank / alpha | 32 / 32(QKVO + MLP 全模組) |
| learning rate | 5e-06(cosine,warmup 0.1,adamw_8bit) |
| num_generations | 8 |
| max prompt / completion length | 256 / 768 |
| steps | 1000 |
| 量化 | 4-bit QLoRA(訓練時) |
| seed | 3407 |
openai/gsm8k(config main)的 train split(7,473 題);
訓練程式只載入 openai/gsm8k(config main) 的 train split(7,473 題);
test split 只由獨立的 eval/run_eval.py 載入。已提交評測固定使用 test 前 200 題,
逐題資料來源、MIT notice、revision、SHA-256 與可重現性限制見
GitHub results provenance。Improved using Qwen. 本模型由Qwen/Qwen2.5-3B-Instruct經 GRPO/QLoRA 微調而成。
LICENSE、LICENSES/ 與 THIRD_PARTY_NOTICES.md。