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ac2. Checkpoint saved
after training step 0 (0-indexed). Strict upstream eval parity:
1100s hard kill, verbatim prompts/entrypoints, group 64x8, T=1.0, kl 0.1.1{
2 "step": 0,
3 "progress/batch": 0,
4 "optim/lr": 4e-05,
5 "progress/done_frac": 0.02,
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7 "puct/sampled_size": 8,
8 "puct/T": 0,
9 "puct/scale_last": 1e-06,
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11 "puct/buffer_value/std": 2.1297770317619676e-15,
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21 "puct/buffer_construction_len/max": 7414.0,
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27 "puct/sampled_timestep/std": 0.0,
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29 "puct/sampled_timestep/max": -1.0,
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31 "puct/sampled_construction_len/std": 1995.2797383763009,
32 "puct/sampled_construction_len/min": 1726.0,
33 "puct/sampled_construction_len/max": 7414.0,
34 "time/sampling": 3206.3807678222656,
35 "env/all/ac_tokens_per_turn": 7496.861328125,
36 "env/all/ob_tokens_per_turn": 4862.0,
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": 3838393,
41 "env/all/total_ob_tokens": 2489344,
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43 "env/all/time/sampling_max": 695.4104588031769,
44 "env/all/time/env_step_mean": 817.8070086957887,
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46 "env/all/reward/mean": 0.36427114852027276,
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49 "env/all/format": 1.0,
50 "env/all/format/min": 1.0,
51 "env/all/format/max": 1.0,
52 "env/all/reward": 0.36427114852027276,
53 "env/all/correctness": 0.4140625,
54 "env/all/correctness/min": 0.0,
55 "env/all/correctness/max": 1.0,
56 "env/all/raw_score": 0.8797491888791493,
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58 "env/all/raw_score/max": 0.9242990420297577,
59 "env/all/initial_raw_score": 0.666666666666666,
60 "env/all/initial_raw_score/min": 0.6666666666666612,
61 "env/all/initial_raw_score/max": 0.6666666666666689,
62 "env/all/msg": "Success; raw_score=0.9057502175519645",
63 "env/all/parsed_code": "```python\n# EVOLVE-BLOCK-START\n\"\"\"Enhanced gradient-based population search with adaptive exploration/exploitation and refined upsampling for maximizing the C2 lower bound.\"\"\"\nimport numpy as np\nfrom typing import Tuple\n\n\ndef _simpson_l2sq(conv: np.ndarray) -> Tuple[float, np.ndarray]:\n \"\"\"\n Compute ||f*f||_2^2 via Simpson-like piecewise-linear rule with endpoint zeros,\n and return its gradient w.r.t conv (same length as conv).\n \"\"\"\n m = conv.size\n if m == 0:\n return 0.0, np.zeros_like(conv)\n\n dx = 1.0 / (m + 1)\n\n y = np.empty(m + 2, dtype=conv.dtype)\n y[0] = 0.0\n y[1:-1] = conv\n y[-1] = 0.0\n\n lhs = y[:-1]\n rhs = y[1:]\n l2_sq = (dx / 3.0) * np.sum(lhs * lhs + lhs * rhs + rhs * rhs)\n\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\n return float(l2_sq), grad_conv\n\n\ndef _l1(conv: np.ndarray) -> Tuple[float, np.ndarray]:\n \"\"\"||f*f||_1 = dx * sum(conv); gradient is dx * ones.\"\"\"\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\n\ndef _linf(conv: np.ndarray) -> Tuple[float, np.ndarray]:\n \"\"\"||f*f||_inf = max(conv); subgradient: uniform over argmax set.\"\"\"\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) / count\n return m, grad\n\n\ndef _objective_and_grad_conv(conv: np.ndarray) -> Tuple[float, np.ndarray]:\n \"\"\"\n Compute C = l2_sq / (l1 * linf) and gradient dC/d(conv) using quotient rule.\n \"\"\"\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 return 0.0, np.zeros_like(conv)\n\n denom = l1 * linf\n c_value = l2_sq / denom\n\n num_grad = g_l2 * denom - l2_sq * (g_l1 * linf + l1 * g_linf)\n g_conv = num_grad / (denom * denom)\n\n return float(c_value), g_conv\n\n\ndef _grad_h_from_conv_grad(h: np.ndarray, g_conv: np.ndarray) -> np.ndarray:\n \"\"\"\n Given dC/d(conv) and conv = h * h (full convolution),\n dC/dh = 2 * (g_conv convolved with reverse(h)) in valid mode (length N).\n \"\"\"\n h_rev = h[::-1]\n g_h = np.convolve(g_conv, h_rev, mode=\"valid\")\n return 2.0 * g_h\n\n\nclass _Adam:\n \"\"\"Lightweight Adam optimizer for numpy arrays (per-candidate).\"\"\"\n\n def __init__(self, shape, lr=3e-2, beta1=0.9, beta2=0.999, eps=1e-8, dtype=np.float32):\n self.m = np.zeros(shape, dtype=dtype)\n self.v = np.zeros(shape, dtype=dtype)\n self.t = 0\n self.lr = lr\n self.b1 = beta1\n self.b2 = beta2\n self.eps = eps\n\n def step(self, params, grad):\n self.t += 1\n self.m = self.b1 * self.m + (1 - self.b1) * grad\n self.v = self.b2 * self.v + (1 - self.b2) * (grad * grad)\n m_hat = self.m / (1 - self.b1 ** self.t)\n v_hat = self.v / (1 - self.b2 ** self.t)\n return params + self.lr * m_hat / (np.sqrt(v_hat) + self.eps)\n\n\ndef _batch_objective(h_batch: np.ndarray) -> Tuple[np.ndarray, list[np.ndarray]]:\n \"\"\"Vectorized objective/conv-grad evaluation over a batch.\"\"\"\n bsz = h_batch.shape[0]\n c_vals = np.zeros(bsz, dtype=np.float32)\n conv_grads = [None] * bsz\n for b in range(bsz):\n h = np.clip(h_batch[b], 0.0, None)\n conv = np.convolve(h, h, mode=\"full\")\n c_val, g_conv = _objective_and_grad_conv(conv)\n c_vals[b] = c_val\n conv_grads[b] = g_conv\n return c_vals, conv_grads\n\n\ndef _phase_update(h_batch, opt_list, lr, add_noise=False, t=0, eta=1e-2, gamma=0.6):\n \"\"\"One optimization step for the whole batch.\"\"\"\n bsz = h_batch.shape[0]\n c_vals, conv_grads = _batch_objective(h_batch)\n grads = np.zeros_like(h_batch, dtype=h_batch.dtype)\n for b in range(bsz):\n clipped = np.clip(h_batch[b], 0.0, None)\n grads[b] = _grad_h_from_conv_grad(clipped, conv_grads[b])\n\n if add_noise:\n sigma = eta / ((t + 1) ** gamma)\n grads = grads + sigma * np.random.normal(size=grads.shape).astype(grads.dtype)\n\n for b in range(bsz):\n opt = opt_list[b]\n opt.lr = lr\n h_new = opt.step(h_batch[b], grads[b].astype(h_batch.dtype))\n h_batch[b] = np.clip(h_new, 0.0, None)\n\n return h_batch, c_vals\n\n\ndef _elitist_respawn(h_batch, c_vals, keep_frac, init_sampler, opt_list):\n \"\"\"Keep top fraction and respawn the rest.\"\"\"\n bsz = h_batch.shape[0]\n keep_n = max(1, int(bsz * keep_frac))\n idx = np.argsort(c_vals)[-keep_n:]\n survivors = h_batch[idx].copy()\n\n fresh = init_sampler(bsz - keep_n)\n new_batch = np.concatenate([survivors, fresh], axis=0)\n\n new_opts = []\n for i in range(keep_n):\n new_opts.append(opt_list[idx[i]])\n for _ in range(bsz - keep_n):\n new_opts.append(_Adam(shape=h_batch.shape[1:], lr=opt_list[0].lr, dtype=h_batch.dtype))\n\n return new_batch, new_opts\n\n\ndef _upsample_1d(h: np.ndarray) -> np.ndarray:\n \"\"\"Linear 2x upsampling on the search grid.\"\"\"\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\n\ndef _single_candidate_finetune(h0: np.ndarray, lr=3e-3, steps=50_000) -> Tuple[np.ndarray, float]:\n \"\"\"Pure exploitation (no noise) on a single vector with Adam + projection.\"\"\"\n h = h0.astype(np.float32).copy()\n opt = _Adam(h.shape, lr=lr, dtype=h.dtype)\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(opt.step(h, g_h.astype(h.dtype)), 0.0, None)\n last_c = c_val\n return h, float(last_c)\n\n\ndef construct_function():\n \"\"\"\n Enhanced four-phase gradient-based search with adaptive exploration/exploitation, refined hyperparameters,\n and multi-stage upsample-refinement for maximizing R(f) = ||f*f||_2^2 / (||f*f||_1 * ||f*f||_inf).\n \"\"\"\n n = 512\n bsz = 64\n total_iter = 20_000\n explore_steps = 10_000\n drop_every = 10_000\n keep_frac = 0.6\n lr_explore = 5e-2\n lr_exploit = 1e-3\n eta, gamma = 1e-2, 0.6\n dtype = np.float32\n\n prev = globals().get(\"GLOBAL_BEST_CONSTRUCTION\")\n if isinstance(prev, (list, tuple, np.ndarray)) and len(prev) > 0:\n h_prev_best = np.array(prev, dtype=dtype)\n else:\n h_prev_best = np.ones(n, dtype=dtype)\n h_prev_best = np.clip(h_prev_best, 0.0, None)\n\n if h_prev_best.shape[0] != n:\n x_old = np.linspace(-0.5, 0.5, h_prev_best.shape[0])\n x_new = np.linspace(-0.5, 0.5, n)\n h_prev_best = np.interp(x_new, x_old, h_prev_best).astype(dtype)\n\n rng = np.random.default_rng()\n\n def init_sampler(m):\n out = rng.uniform(0.0, 1.0, size=(m, n)).astype(dtype)\n if m > 0:\n out[0] = h_prev_best\n return out\n\n h_batch = init_sampler(bsz)\n opt_list = [_Adam(shape=(n,), lr=lr_explore, dtype=dtype) for _ in range(bsz)]\n best_h = h_batch.copy()\n best_c = np.full(bsz, -np.inf, dtype=dtype)\n\n print(\"Starting population-based optimization...\")\n for t in range(total_iter):\n if t < explore_steps:\n h_batch, c_vals = _phase_update(\n h_batch, opt_list, lr=lr_explore, add_noise=True, t=t, eta=eta, gamma=gamma\n )\n print(f\"Exploration phase iteration {t+1}/{total_iter}: Best score {np.max(best_c):.6f}\")\n else:\n h_batch, c_vals = _phase_update(\n h_batch, opt_list, lr=lr_exploit, add_noise=False, t=t, eta=eta, gamma=gamma\n )\n print(f\"Exploitation phase iteration {t+1}/{total_iter}: Best score {np.max(best_c):.6f}\")\n\n improved = c_vals > best_c\n best_c = np.where(improved, c_vals, best_c)\n best_h[improved] = h_batch[improved]\n\n if (t + 1) % drop_every == 0:\n print(f\"Elitist respawn at iteration {t+1}\")\n h_batch, opt_list = _elitist_respawn(\n h_batch, c_vals, keep_frac=keep_frac, init_sampler=init_sampler, opt_list=opt_list\n )\n\n idx = int(np.argmax(best_c))\n h_star = np.clip(best_h[idx].astype(np.float32), 0.0, None)\n\n print(\"Starting first upsampling and fine-tuning...\")\n h_up1 = _upsample_1d(h_star)\n h_up1, _ = _single_candidate_finetune(h_up1, lr=3e-3, steps=60_000)\n print(f\"First fine-tune score: {evaluate_sequence(h_up1.tolist()):.6f}\")\n\n print(\"Starting second upsampling and fine-tuning...\")\n h_up2 = _upsample_1d(h_up1)\n h_up2, _ = _single_candidate_finetune(h_up2, lr=3e-3, steps=60_000)\n print(f\"Second fine-tune score: {evaluate_sequence(h_up2.tolist()):.6f}\")\n\n print(\"Starting third upsampling and fine-tuning...\")\n h_up3 = _upsample_1d(h_up2)\n h_up3, _ = _single_candidate_finetune(h_up3, lr=3e-3, steps=60_000)\n print(f\"Third fine-tune score: {evaluate_sequence(h_up3.tolist()):.6f}\")\n\n heights = np.clip(h_up3, 0.0, None)\n r_value = evaluate_sequence(heights.tolist())\n print(f\"Final C2 lower bound: {r_value:.6f}\")\n return heights.tolist()\n# EVOLVE-BLOCK-END\n```",
64 "env/all/time/policy": 517.772373217158,
65 "env/all/time/policy/min": 232.84041047096252,
66 "env/all/time/policy/max": 695.4104588031769,
67 "env/all/time/env_step": 817.8070086957887,
68 "env/all/time/env_step/min": 0.0064237117767333984,
69 "env/all/time/env_step/max": 2513.324860572815,
70 "env/all/time/reward_compute": 3.501772880554199e-07,
71 "env/all/time/reward_compute/min": 2.2724270820617676e-07,
72 "env/all/time/reward_compute/max": 8.866190910339355e-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": 0.02090245857834816,
77 "advantage/min": -0.9966553449630737,
78 "advantage/max": 2.1692190170288086,
79 "time/assemble_training_data": 10.06288743019104,
80 "time/kl_vs_base": 149.8704798221588,
81 "kl_policy_base": 0.0004154869238846004,
82 "time/train": 1185.0928881168365,
83 "time/save_checkpoint": 18.647024869918823,
84 "time/total": 4574.345517873764
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