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ac2. Checkpoint saved
after training step 22 (0-indexed). Strict upstream eval parity:
1100s hard kill, verbatim prompts/entrypoints, group 64x8, T=1.0, kl 0.1.1{
2 "step": 22,
3 "progress/batch": 22,
4 "optim/lr": 4e-05,
5 "progress/done_frac": 0.46,
6 "puct/buffer_size": 358,
7 "puct/sampled_size": 8,
8 "puct/T": 11264,
9 "puct/scale_last": 0.4389755876482807,
10 "puct/buffer_value/mean": 0.9261315037941318,
11 "puct/buffer_value/std": 0.05058345562593805,
12 "puct/buffer_value/min": 0.5041471954736918,
13 "puct/buffer_value/max": 0.9431227831219725,
14 "puct/buffer_timestep/mean": 10.19832402234637,
15 "puct/buffer_timestep/std": 6.486108704735559,
16 "puct/buffer_timestep/min": -1.0,
17 "puct/buffer_timestep/max": 21.0,
18 "puct/buffer_construction_len/mean": 2669.469273743017,
19 "puct/buffer_construction_len/std": 2845.9031492813347,
20 "puct/buffer_construction_len/min": 1024.0,
21 "puct/buffer_construction_len/max": 32768.0,
22 "puct/sampled_value/mean": 0.9430820012833923,
23 "puct/sampled_value/std": 2.0573896125090993e-05,
24 "puct/sampled_value/min": 0.9430612419552252,
25 "puct/sampled_value/max": 0.9431227831219725,
26 "puct/sampled_timestep/mean": 21.0,
27 "puct/sampled_timestep/std": 0.0,
28 "puct/sampled_timestep/min": 21.0,
29 "puct/sampled_timestep/max": 21.0,
30 "puct/sampled_construction_len/mean": 2048.0,
31 "puct/sampled_construction_len/std": 0.0,
32 "puct/sampled_construction_len/min": 2048.0,
33 "puct/sampled_construction_len/max": 2048.0,
34 "time/sampling": 4238.19649720192,
35 "env/all/ac_tokens_per_turn": 9180.1796875,
36 "env/all/ob_tokens_per_turn": 3711.0,
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": 4700252,
41 "env/all/total_ob_tokens": 1900032,
42 "env/all/time/sampling_mean": 357.74763314053416,
43 "env/all/time/sampling_max": 459.64632987976074,
44 "env/all/time/env_step_mean": 1641.6257998142391,
45 "env/all/time/env_step_max": 3775.6860406398773,
46 "env/all/reward/mean": 0.5991520039300096,
47 "env/all/reward/max": 0.9431813549921008,
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": 0.5991520039300096,
53 "env/all/correctness": 0.673828125,
54 "env/all/correctness/min": 0.0,
55 "env/all/correctness/max": 1.0,
56 "env/all/raw_score": 0.8891763072816372,
57 "env/all/raw_score/min": 0.23729818094701602,
58 "env/all/raw_score/max": 0.9431813549921008,
59 "env/all/initial_raw_score": 0.9430820012833925,
60 "env/all/initial_raw_score/min": 0.9430612419552252,
61 "env/all/initial_raw_score/max": 0.9431227831219725,
62 "env/all/msg": "RuntimeError: Program execution failed: ValueError: Sum of sequence is too close to zero.",
63 "env/all/parsed_code": "```python\nimport numpy as np\nimport time\nfrom typing import Tuple\nimport random\n\ndef _simpson_l2sq(conv: np.ndarray) -> Tuple[float, np.ndarray]:\n \"\"\"Compute ||f*f||_2^2 via Simpson's rule with endpoint zeros and gradient.\"\"\"\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 \"\"\"Compute ||f*f||_1 and its gradient.\"\"\"\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 \"\"\"Compute ||f*f||_inf and its subgradient.\"\"\"\n if conv.size == 0:\n return 0.0, np.zeros_like(conv)\n m = float(np.max(conv))\n mask = conv == m\n count = int(mask.sum())\n if count == 0 or m <= 0.0:\n return m, np.zeros_like(conv)\n grad = mask.astype(conv.dtype)\n return m, grad\n\ndef _objective_and_grad_conv(conv: np.ndarray) -> Tuple[float, np.ndarray]:\n \"\"\"Compute C = l2_sq / (l1 * linf) and its gradient.\"\"\"\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 \"\"\"Compute gradient of C w.r.t h from gradient of C w.r.t conv.\"\"\"\n h_rev = h[::-1]\n g_h = np.convolve(g_conv, h_rev, mode=\"valid\")\n return 2.0 * g_h\n\ndef construct_function():\n \"\"\"\n Optimizes step function sequence using a multi-scale multi-start approach, combining \n structured initializations, adaptive Adam with dynamic constraint handling, and \n advanced gradient-based optimization with enhanced initialization diversity.\n \"\"\"\n n_start = 64\n max_seconds = 1000\n num_initializations = 32 # Increase diversity\n \n # Initialize from previous best if available\n initial_sequences = []\n if 'height_sequence_1' in globals():\n height_seq = np.array(height_sequence_1, dtype=np.float32)\n height_seq_sum = np.sum(height_seq)\n if height_seq_sum < 0.01:\n scale = 0.01 / height_seq_sum\n height_seq = height_seq * scale\n initial_sequences.append(height_seq)\n else:\n pass # No initial sequence available\n \n # Generate diverse structured initial sequences with exact sum 0.01\n for _ in range(num_initializations):\n seq = np.full(n_start, 0.01 / n_start, dtype=np.float32)\n seq_sum = np.sum(seq)\n if seq_sum < 0.01:\n scale = 0.01 / seq_sum\n seq = seq * scale\n seq = np.clip(seq, 0.0, 1000.0)\n initial_sequences.append(seq)\n \n seq = np.zeros(n_start, dtype=np.float32)\n num_random_peaks = 8\n val = 0.01 / num_random_peaks\n indices = np.random.choice(n_start, num_random_peaks, replace=False)\n seq[indices] = val\n seq_sum = np.sum(seq)\n if seq_sum < 0.01:\n scale = 0.01 / seq_sum\n seq = seq * scale\n seq = np.clip(seq, 0.0, 1000.0)\n initial_sequences.append(seq)\n \n seq = np.zeros(n_start, dtype=np.float32)\n seq[n_start // 2] = 0.01\n seq_sum = np.sum(seq)\n if seq_sum < 0.01:\n scale = 0.01 / seq_sum\n seq = seq * scale\n seq = np.clip(seq, 0.0, 1000.0)\n initial_sequences.append(seq)\n \n seq = np.zeros(n_start, dtype=np.float32)\n seq = 1000.0 * np.abs(np.sin(np.linspace(0, 2 * np.pi, n_start)))\n seq_sum = np.sum(seq)\n if seq_sum < 0.01:\n scale = 0.01 / seq_sum\n seq = seq * scale\n seq = np.clip(seq, 0.0, 1000.0)\n initial_sequences.append(seq)\n \n seq = np.zeros(n_start, dtype=np.float32)\n center = n_start // 2\n std = n_start // 8\n for i in range(n_start):\n seq[i] = 1000.0 * np.exp(-((i - center)**2) / (2 * std**2))\n seq_sum = np.sum(seq)\n if seq_sum < 0.01:\n scale = 0.01 / seq_sum\n seq = seq * scale\n seq = np.clip(seq, 0.0, 1000.0)\n initial_sequences.append(seq)\n \n seq = np.zeros(n_start, dtype=np.float32)\n half = n_start // 2\n seq[:half] = 0.01 / half\n seq_sum = np.sum(seq)\n if seq_sum < 0.01:\n scale = 0.01 / seq_sum\n seq = seq * scale\n seq = np.clip(seq, 0.0, 1000.0)\n initial_sequences.append(seq)\n \n # Evaluate initial sequences to find the best one\n best_seq = None\n best_value = -float('inf')\n for seq in initial_sequences:\n try:\n current_value = evaluate_sequence(seq.tolist())\n except Exception as e:\n print(f\"Error evaluating initial sequence: {e}\")\n continue\n if current_value > best_value:\n best_value = current_value\n best_seq = seq.copy()\n \n if best_seq is None:\n # Fallback to random initialization with exact sum\n best_seq = np.random.rand(n_start) * 1000.0\n best_seq = np.clip(best_seq, 0.0, 1000.0)\n best_seq_sum = np.sum(best_seq)\n if best_seq_sum > 0.0:\n scale = 0.01 / best_seq_sum\n best_seq = best_seq * scale\n best_value = evaluate_sequence(best_seq.tolist())\n \n # Multi-scale optimization: upscale to target length\n target_length = 2048\n for scale in range(1, 4): # Upscale 3 times\n current_length = n_start * (2 ** scale)\n if current_length > target_length:\n break\n upsampled_seq = np.repeat(best_seq, 2, axis=0)\n upsampled_seq = upsampled_seq[:target_length]\n sum_ = np.sum(upsampled_seq)\n if sum_ < 0.01:\n needed = 0.01 - sum_\n upsampled_seq += needed / len(upsampled_seq)\n else:\n scale_factor = 0.01 / sum_\n upsampled_seq = upsampled_seq * scale_factor\n upsampled_seq = np.clip(upsampled_seq, 0.0, 1000.0)\n \n # Evaluate upscaled sequence\n upscaled_value = evaluate_sequence(upsampled_seq.tolist())\n if upscaled_value > best_value:\n best_value = upscaled_value\n best_seq = upsampled_seq.copy()\n \n # Final optimization with Adam with improved hyperparameters\n start_time = time.time()\n best_seq = best_seq.copy()\n best_value = evaluate_sequence(best_seq.tolist())\n \n # Parameters for optimization\n max_iter = 200000 # Maximum iterations\n learning_rate = 0.1 # Initial learning rate\n decay_rate = 0.998 # Learning rate decay\n min_lr = 1e-5 # Minimum learning rate\n decay_steps = 50 # Decay steps\n step_counter = 0\n beta1 = 0.9\n beta2 = 0.999\n epsilon = 1e-8\n\n # Adam parameters\n m = np.zeros_like(best_seq)\n v = np.zeros_like(best_seq)\n t = 0\n\n for _ in range(max_iter):\n step_counter += 1\n \n # Compute gradient\n conv = np.convolve(best_seq, best_seq, mode='full')\n l2_sq, g_l2 = _simpson_l2sq(conv)\n l1, g_l1 = _l1(conv)\n linf, g_linf = _linf(conv)\n \n if l1 <= 0.0 or linf <= 0.0:\n break # Avoid division by zero\n \n denom = l1 * linf\n c_value = l2_sq / denom\n \n # Compute gradient of C with respect to conv\n g_conv = _objective_and_grad_conv(conv)[1]\n \n # Compute gradient of C with respect to h\n grad_h = _grad_h_from_conv_grad(best_seq, g_conv) # Use unclamped sequence\n \n # Adam update\n t += 1\n m = beta1 * m + (1 - beta1) * grad_h\n v = beta2 * v + (1 - beta2) * (grad_h ** 2)\n m_hat = m / (1 - beta1 ** t)\n v_hat = v / (1 - beta2 ** t)\n step = learning_rate * m_hat / (np.sqrt(v_hat) + epsilon)\n best_seq = best_seq + step\n \n # Clip parameters and maintain sum constraint\n best_seq = np.clip(best_seq, 0.0, 1000.0)\n sum_seq = np.sum(best_seq)\n if sum_seq < 0.01:\n if sum_seq > 0.0:\n scale = 0.01 / sum_seq\n best_seq = best_seq * scale\n best_seq = np.clip(best_seq, 0.0, 1000.0)\n \n # Track best value\n current_value = evaluate_sequence(best_seq.tolist())\n if current_value > best_value:\n best_value = current_value\n best_seq = best_seq.copy()\n \n # Check time limit\n if time.time() - start_time > max_seconds - 5:\n break\n \n # Decay learning rate\n if step_counter % decay_steps == 0:\n learning_rate *= decay_rate\n\n # Final evaluation\n final_value = evaluate_sequence(best_seq.tolist())\n print(f\"Final C2 lower bound: {final_value:.6f}\")\n return best_seq.tolist()\n```",
64 "env/all/time/policy": 357.74763314053416,
65 "env/all/time/policy/min": 153.37775826454163,
66 "env/all/time/policy/max": 459.64632987976074,
67 "env/all/time/env_step": 1641.6257998142391,
68 "env/all/time/env_step/min": 0.006284236907958984,
69 "env/all/time/env_step/max": 3775.6860406398773,
70 "env/all/time/reward_compute": 2.975575625896454e-07,
71 "env/all/time/reward_compute/min": 2.1606683731079102e-07,
72 "env/all/time/reward_compute/max": 5.327165126800537e-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": 0.017695410177111626,
77 "advantage/min": -1.0,
78 "advantage/max": 1.6911075115203857,
79 "time/assemble_training_data": 6.2939887046813965,
80 "time/kl_vs_base": 92.44419145584106,
81 "kl_policy_base": 0.000759115326218307,
82 "time/train": 627.1410887241364,
83 "time/save_checkpoint": 17.785562753677368,
84 "time/total": 4984.0259783267975
85}[2026-07-09T06:38:34+00:00] job=1812632 node=node-30 ngpu=3 ntrain=1 replicas=2 flash_attn=no
[2026-07-09T06:45:52+00:00] job=1812704 node=node-30 ngpu=3 ntrain=1 replicas=2 flash_attn=no
[2026-07-09T07:00:42+00:00] job=1812735 node=node-1 ngpu=3 ntrain=1 replicas=2 flash_attn=no
[2026-07-09T07:26:33+00:00] job=1812827 node=node-14 ngpu=3 ntrain=1 replicas=2 flash_attn=yes
[2026-07-09T09:21:09+00:00] job=1813131 node=node-1 ngpu=3 ntrain=1 replicas=2 flash_attn=yes
[2026-07-09T14:53:48+00:00] job=1813132 node=node-2 ngpu=6 ntrain=2 replicas=4 flash_attn=yes
[2026-07-10T03:31:51+00:00] job=1816627 node=node-14 ngpu=3 ntrain=1 replicas=2 flash_attn=yes
[2026-07-10T03:54:03+00:00] job=1816628 node=node-29 ngpu=6 ntrain=2 replicas=4 flash_attn=yes
[2026-07-10T07:56:44+00:00] job=1817463 node=node-27 ngpu=3 ntrain=1 replicas=2 flash_attn=yes
[2026-07-10T09:10:26+00:00] job=1817464 node=node-7 ngpu=6 ntrain=2 replicas=4 flash_attn=yes