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ac2. Checkpoint saved
after training step 21 (0-indexed). Strict upstream eval parity:
1100s hard kill, verbatim prompts/entrypoints, group 64x8, T=1.0, kl 0.1.1{
2 "step": 21,
3 "progress/batch": 21,
4 "optim/lr": 4e-05,
5 "progress/done_frac": 0.44,
6 "puct/buffer_size": 341,
7 "puct/sampled_size": 8,
8 "puct/T": 10752,
9 "puct/scale_last": 0.19101914732870273,
10 "puct/buffer_value/mean": 0.928978759511534,
11 "puct/buffer_value/std": 0.04624274817349878,
12 "puct/buffer_value/min": 0.6666666666666636,
13 "puct/buffer_value/max": 0.9474750075686938,
14 "puct/buffer_timestep/mean": 9.803519061583577,
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16 "puct/buffer_timestep/min": -1.0,
17 "puct/buffer_timestep/max": 20.0,
18 "puct/buffer_construction_len/mean": 3675.0469208211143,
19 "puct/buffer_construction_len/std": 2163.5975710402663,
20 "puct/buffer_construction_len/min": 1024.0,
21 "puct/buffer_construction_len/max": 32768.0,
22 "puct/sampled_value/mean": 0.9474742145392451,
23 "puct/sampled_value/std": 4.423072216599454e-07,
24 "puct/sampled_value/min": 0.9474737647675918,
25 "puct/sampled_value/max": 0.9474750075686938,
26 "puct/sampled_timestep/mean": 20.0,
27 "puct/sampled_timestep/std": 0.0,
28 "puct/sampled_timestep/min": 20.0,
29 "puct/sampled_timestep/max": 20.0,
30 "puct/sampled_construction_len/mean": 4096.0,
31 "puct/sampled_construction_len/std": 0.0,
32 "puct/sampled_construction_len/min": 4096.0,
33 "puct/sampled_construction_len/max": 4096.0,
34 "time/sampling": 4563.946535587311,
35 "env/all/ac_tokens_per_turn": 8414.998046875,
36 "env/all/ob_tokens_per_turn": 3632.875,
37 "env/all/turns_per_episode": 1.0,
38 "env/all/total_episodes": 512,
39 "env/all/total_turns": 512,
40 "env/all/total_ac_tokens": 4308479,
41 "env/all/total_ob_tokens": 1860032,
42 "env/all/time/sampling_mean": 312.9978525242768,
43 "env/all/time/sampling_max": 420.68243622779846,
44 "env/all/time/env_step_mean": 1952.3124936767854,
45 "env/all/time/env_step_max": 4142.032496213913,
46 "env/all/reward/mean": 0.5869454936579409,
47 "env/all/reward/max": 0.9474752129733522,
48 "env/all/reward/min": 0.0,
49 "env/all/format": 1.0,
50 "env/all/format/min": 1.0,
51 "env/all/format/max": 1.0,
52 "env/all/reward": 0.5869454936579409,
53 "env/all/correctness": 0.634765625,
54 "env/all/correctness/min": 0.0,
55 "env/all/correctness/max": 1.0,
56 "env/all/raw_score": 0.9246649007780484,
57 "env/all/raw_score/min": 0.02357264747619004,
58 "env/all/raw_score/max": 0.9474752129733522,
59 "env/all/initial_raw_score": 0.9474742145392449,
60 "env/all/initial_raw_score/min": 0.9474737647675918,
61 "env/all/initial_raw_score/max": 0.9474750075686938,
62 "env/all/msg": "Success; raw_score=0.9474750277873087",
63 "env/all/parsed_code": "```python\nimport numpy as np\nimport time\nfrom typing import List, Tuple\nimport random\n\ndef _simpson_l2sq(conv: np.ndarray) -> Tuple[float, np.ndarray]:\n m = conv.size\n if m == 0:\n return 0.0, np.zeros_like(conv)\n dx = 1.0 / (m + 1)\n y = np.zeros(m + 2, dtype=conv.dtype)\n y[0] = 0.0\n y[1:-1] = conv\n y[-1] = 0.0\n lhs = y[:-1]\n rhs = y[1:]\n l2_sq = (dx / 3.0) * np.sum(lhs * lhs + lhs * rhs + rhs * rhs)\n grad_y = (dx / 3.0) * (4.0 * y + np.roll(y, 1) + np.roll(y, -1))\n grad_conv = grad_y[1:-1]\n return float(l2_sq), grad_conv\n\ndef _l1(conv: np.ndarray) -> Tuple[float, np.ndarray]:\n m = conv.size\n dx = 1.0 / (m + 1) if m > 0 else 1.0\n val = dx * float(np.sum(conv)) if m > 0 else 0.0\n grad = np.full_like(conv, dx)\n return val, grad\n\ndef _linf(conv: np.ndarray) -> Tuple[float, np.ndarray]:\n if conv.size == 0:\n return 0.0, np.zeros_like(conv)\n m = float(np.max(conv))\n if m <= 0.0:\n return m, np.zeros_like(conv)\n mask = conv == m\n count = int(mask.sum())\n if count == 0:\n return m, np.zeros_like(conv)\n grad = mask.astype(conv.dtype) / count\n return m, grad\n\ndef _objective_and_grad_conv(conv: np.ndarray) -> Tuple[float, np.ndarray]:\n l2_sq, g_l2 = _simpson_l2sq(conv)\n l1, g_l1 = _l1(conv)\n linf, g_linf = _linf(conv)\n if l1 <= 0.0 or linf <= 0.0:\n return 0.0, np.zeros_like(conv)\n denom = l1 * linf\n c_value = l2_sq / denom\n num_grad = g_l2 * denom - l2_sq * (g_l1 * linf + l1 * g_linf)\n g_conv = num_grad / (denom * denom)\n return float(c_value), g_conv\n\ndef _grad_h_from_conv_grad(h: np.ndarray, g_conv: np.ndarray) -> np.ndarray:\n h_rev = h[::-1]\n g_h = np.convolve(g_conv, h_rev, mode=\"valid\")\n return 2.0 * g_h\n\ndef _upsample_1d(h: np.ndarray) -> np.ndarray:\n n = h.shape[0]\n x_old = np.linspace(-0.5, 0.5, n)\n x_new = np.linspace(-0.5, 0.5, 2 * n)\n return np.interp(x_new, x_old, h)\n\ndef _single_candidate_finetune(h0: np.ndarray, lr=1e-4, steps=300000) -> Tuple[np.ndarray, float]:\n h = h0.astype(np.float32).copy()\n last_c = 0.0\n for _ in range(steps):\n h_clip = np.clip(h, 0.0, None)\n conv = np.convolve(h_clip, h_clip, mode=\"full\")\n c_val, g_conv = _objective_and_grad_conv(conv)\n g_h = _grad_h_from_conv_grad(h_clip, g_conv)\n h = np.clip(h + lr * g_h, 0.0, 1000.0)\n last_c = c_val\n return h, float(last_c)\n\ndef construct_function():\n \"\"\"\n Optimizes a sequence of non-negative heights using a refined multi-scale approach with\n improved initialization, adaptive exploration, and dynamic learning rate strategies.\n Starts with a triangular initial guess to promote concentrated convolution, gradually\n upscaling, adapting noise and learning rate for global exploration, and applying\n fine-tuned gradient ascent to maximize the evaluation score.\n \"\"\"\n np.random.seed(42)\n\n # Load previous best sequence if available\n height_sequence_1 = globals().get(\"height_sequence_1\", None)\n target_length = 4096\n initial_n = 128 # Starting sequence length\n\n if height_sequence_1 is not None:\n # Use the previous best and upscale\n initial_h = np.array(height_sequence_1, dtype=np.float32)\n initial_n = min(len(initial_h), target_length)\n x_old = np.linspace(-0.5, 0.5, len(initial_h))\n x_new = np.linspace(-0.5, 0.5, initial_n)\n h = np.interp(x_new, x_old, initial_h)\n else:\n # Generate a triangular initial sequence\n h = np.zeros(target_length)\n for i in range(target_length):\n if i <= target_length // 2:\n h[i] = i / (target_length // 2)\n else:\n h[i] = (target_length - i) / (target_length // 2)\n h_sum = np.sum(h)\n if h_sum < 0.01:\n scale_factor = 0.01 / h_sum\n h = np.clip(h * scale_factor, 0.0, 1000.0)\n # Trim to initial_n and pad\n x_old = np.linspace(-0.5, 0.5, target_length)\n x_new = np.linspace(-0.5, 0.5, initial_n)\n h = np.interp(x_new, x_old, h)\n h = np.pad(h, (0, target_length - initial_n), 'constant', constant_values=0)\n\n # Parameters for enhanced exploration\n learning_rate = 0.5\n noise_scale_initial = 10.0 # Reduced initial noise scale\n noise_decay = 0.9999 # Faster noise decay\n learning_rate_decay = 0.999 # Slightly slower learning rate decay\n max_steps = 250000 # Reduced max steps for faster convergence\n upscale_steps = 250000 # Increased steps for upscaling\n refine_steps = 300000\n\n start_time = time.time()\n\n # Multi-scale optimization with enhanced exploration\n current_length = initial_n\n scale_factor = 2\n\n for _ in range(6): # Upscale 6 times to reach 4096\n if current_length < target_length:\n h = _upsample_1d(h)\n current_length *= 2\n # Ensure sum is above 0.01\n h_sum = np.sum(h)\n if h_sum < 0.01:\n h = np.clip(h + (0.01 - h_sum) / h.shape[0], 0.0, 1000.0)\n \n # Initial optimization with dynamic noise and learning rate\n h_opt = np.copy(h)\n for step in range(50000): # Increased steps for better optimization\n # Clip to non-negative\n clipped_h = np.clip(h_opt, 0.0, None)\n \n # Compute convolution\n conv = np.convolve(clipped_h, clipped_h, mode='full')\n \n # Compute objective and gradient of conv\n obj_val, grad_conv = _objective_and_grad_conv(conv)\n \n # Compute gradient with respect to h\n grad_h = _grad_h_from_conv_grad(clipped_h, grad_conv)\n \n # Adjust noise and learning rate based on step\n noise_scale = noise_scale_initial * (noise_decay ** step)\n learning_rate_current = learning_rate * (learning_rate_decay ** step)\n \n # Add noise\n noise = noise_scale * np.random.normal(size=h_opt.shape)\n \n # Update h with adaptive noise and learning\n h_opt = np.clip(h_opt + learning_rate_current * grad_h + noise, 0.0, 1000.0)\n \n # Maintain sum requirement dynamically\n h_sum_current = np.sum(h_opt)\n if h_sum_current < 0.01:\n scale_factor = 0.01 / h_sum_current\n h_opt = np.clip(h_opt * scale_factor, 0.0, 1000.0)\n \n # Check time remaining\n remaining_time = 1000 - (time.time() - start_time)\n if remaining_time < 5:\n print(f\"Time remaining: {remaining_time} seconds. Performing final refinement.\")\n break\n # Assign optimized h\n h = h_opt\n \n # Final refined optimization with smaller learning rate\n h_refined, _ = _single_candidate_finetune(h, lr=1e-4, steps=refine_steps)\n \n # Final check and normalization\n h_final = np.clip(h_refined, 0.0, 1000.0)\n heights = h_final.tolist()\n r_value = evaluate_sequence(heights)\n print(f\"Final C2 lower bound: {r_value}\")\n return heights\n```",
64 "env/all/time/policy": 312.9978525242768,
65 "env/all/time/policy/min": 160.37055373191833,
66 "env/all/time/policy/max": 420.68243622779846,
67 "env/all/time/env_step": 1952.3124936767854,
68 "env/all/time/env_step/min": 0.005844831466674805,
69 "env/all/time/env_step/max": 4142.032496213913,
70 "env/all/time/reward_compute": 3.059394657611847e-07,
71 "env/all/time/reward_compute/min": 2.0116567611694336e-07,
72 "env/all/time/reward_compute/max": 7.413327693939209e-07,
73 "env/all/by_group/frac_mixed": 1.0,
74 "env/all/by_group/frac_all_good": 0.0,
75 "env/all/by_group/frac_all_bad": 0.0,
76 "advantage/mean": 0.013279522769153118,
77 "advantage/min": -1.0,
78 "advantage/max": 1.315354585647583,
79 "time/assemble_training_data": 10.625341653823853,
80 "time/kl_vs_base": 89.66139960289001,
81 "kl_policy_base": 0.000718804367352277,
82 "time/train": 581.3122353553772,
83 "time/save_checkpoint": 14.946512460708618,
84 "time/total": 5264.1647889614105
85}[2026-07-09T06:42:12+00:00] job=1812634 node=node-30 ngpu=3 ntrain=1 replicas=2 flash_attn=no
[2026-07-09T07:26:33+00:00] job=1812736 node=node-12 ngpu=3 ntrain=1 replicas=2 flash_attn=yes
[2026-07-09T09:27:32+00:00] job=1813133 node=node-14 ngpu=3 ntrain=1 replicas=2 flash_attn=yes
[2026-07-09T16:26:04+00:00] job=1813134 node=node-4 ngpu=6 ntrain=2 replicas=4 flash_attn=yes