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ac1. Checkpoint saved
after training step 34 (0-indexed). Strict upstream eval parity:
1100s hard kill, verbatim prompts/entrypoints, group 64x8, T=1.0, kl 0.1.1{
2 "step": 34,
3 "progress/batch": 34,
4 "optim/lr": 4e-05,
5 "progress/done_frac": 0.7,
6 "puct/buffer_size": 552,
7 "puct/sampled_size": 8,
8 "puct/T": 17408,
9 "puct/scale_last": 0.5749009802170184,
10 "puct/buffer_value/mean": -1.5247558878122511,
11 "puct/buffer_value/std": 0.084519541392093,
12 "puct/buffer_value/min": -2.0797874683482562,
13 "puct/buffer_value/max": -1.5048864881312378,
14 "puct/buffer_timestep/mean": 16.246376811594203,
15 "puct/buffer_timestep/std": 9.96138311376349,
16 "puct/buffer_timestep/min": -1.0,
17 "puct/buffer_timestep/max": 33.0,
18 "puct/buffer_construction_len/mean": 1108.677536231884,
19 "puct/buffer_construction_len/std": 701.7602874839685,
20 "puct/buffer_construction_len/min": 1000.0,
21 "puct/buffer_construction_len/max": 7850.0,
22 "puct/sampled_value/mean": -1.5052793224847207,
23 "puct/sampled_value/std": 0.00037564471185891314,
24 "puct/sampled_value/min": -1.5056547838651437,
25 "puct/sampled_value/max": -1.5048864881312378,
26 "puct/sampled_timestep/mean": 33.0,
27 "puct/sampled_timestep/std": 0.0,
28 "puct/sampled_timestep/min": 33.0,
29 "puct/sampled_timestep/max": 33.0,
30 "puct/sampled_construction_len/mean": 1000.0,
31 "puct/sampled_construction_len/std": 0.0,
32 "puct/sampled_construction_len/min": 1000.0,
33 "puct/sampled_construction_len/max": 1000.0,
34 "time/sampling": 6394.787328004837,
35 "env/all/ac_tokens_per_turn": 8301.64453125,
36 "env/all/ob_tokens_per_turn": 3802.0,
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": 4250442,
41 "env/all/total_ob_tokens": 1946624,
42 "env/all/time/sampling_mean": 539.9991992157884,
43 "env/all/time/sampling_max": 767.7666115760803,
44 "env/all/time/env_step_mean": 2943.3939134194516,
45 "env/all/time/env_step_max": 5627.398071527481,
46 "env/all/reward/mean": 0.584961352896587,
47 "env/all/reward/max": 0.6645284951282326,
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": 0.584961352896587,
53 "env/all/correctness": 0.896484375,
54 "env/all/correctness/min": 0.0,
55 "env/all/correctness/max": 1.0,
56 "env/all/raw_score": 1.646701626543987,
57 "env/all/raw_score/min": 1.5048263553570373,
58 "env/all/raw_score/max": 50.8589537049047,
59 "env/all/initial_raw_score": -1.5052793224847205,
60 "env/all/initial_raw_score/min": -1.5056547838651437,
61 "env/all/initial_raw_score/max": -1.5048864881312378,
62 "env/all/msg": "Success; raw_score=1.5048843194775876",
63 "env/all/parsed_code": "```python\nimport time\nimport numpy as np\nimport cvxpy as cp\nimport copy\nimport random\n\ndef propose_candidate(seed=42, budget_s=1000, **kwargs):\n \"\"\"\n Optimizes a sequence of non-negative coefficients to minimize the evaluation function.\n Uses a hybrid approach combining enhanced structured initialization, modified LP-based optimization,\n adaptive perturbation, and multi-phase exploration to escape local minima.\n \"\"\"\n np.random.seed(seed)\n deadline = time.time() + budget_s - 10\n\n # Try to start from known best construction\n prev = globals().get(\"height_sequence_1\", None)\n if prev is not None and isinstance(prev, (list, np.ndarray)) and len(prev) > 0:\n best_sequence = list(np.asarray(prev, dtype=float))\n else:\n # Generate diverse and informative initial sequences\n initial_sequences = []\n for _ in range(100): # More thorough initial sampling\n base_len = np.random.choice([256, 512, 1024, 1500])\n base = np.zeros(base_len)\n if np.random.rand() < 0.03: # Gauss-like decay\n base = np.exp(-np.linspace(0, 1, base_len)**2 * 10) * 0.1 + 0.02\n elif np.random.rand() < 0.03: # Multi-peak decay\n base = np.zeros_like(base)\n num_peaks = np.random.randint(3, 7)\n for i in range(num_peaks):\n x = np.random.uniform(0, 1)\n base += np.exp(-((np.linspace(0, 1, base_len) - x)**2)*10) * 0.1\n base *= 0.2\n elif np.random.rand() < 0.03: # Random wave with high amplitude\n base = np.sin(np.linspace(0, 4 * np.pi, base_len)) * 0.1 + 0.05\n elif np.random.rand() < 0.03: # Exponential decay\n base = np.random.exponential(scale=0.2, size=base_len) * 0.05\n else: # Random but structured\n base = np.random.rand(base_len) * 0.5 + np.exp(-np.linspace(0, 1, base_len)**2 * 10) * 0.1\n sum_base = np.sum(base)\n scale_factor = 0.8 / (sum_base if sum_base > 0.0 else 0.5) \n initial_sequences.append([max(0.0, x * scale_factor) for x in base])\n best_sequence = initial_sequences[0].copy()\n for seq in initial_sequences:\n curr_score = evaluate_sequence(seq)\n best_score = evaluate_sequence(best_sequence)\n if curr_score < best_score:\n best_sequence = seq.copy()\n\n current_sequence = best_sequence.copy()\n best_score = float('inf')\n\n def perturb_sequence(seq):\n \"\"\"Strategic element-wise adjustments to reduce peak convolution values.\"\"\"\n n = len(seq)\n if n == 0:\n return None\n conv = np.convolve(seq, seq)\n top_indices = np.argsort(conv)[-200:] # Top 200 convolution positions\n indices = np.random.choice(top_indices, size=min(100, len(top_indices)), replace=False)\n indices = [min(idx, len(seq)-1) for idx in indices]\n if np.random.rand() < 0.15:\n random_idx = np.random.randint(0, len(seq))\n indices.append(random_idx)\n scale = 0.1 * np.std(seq) # Smaller perturbations\n for idx in indices:\n new_val = max(0.0, seq[idx] - np.random.normal(0, scale))\n seq = [new_val if i == idx else seq[i] for i in range(len(seq))]\n return seq\n\n def random_walk(seq):\n \"\"\"Small random perturbations to all elements for exploration.\"\"\"\n scale = 0.05 * np.std(seq)\n new_seq = [max(0.0, x + np.random.normal(0, scale)) for x in seq]\n return new_seq\n\n def get_good_direction_to_move_into(sequence):\n \"\"\"Optimizes through dynamic constraint selection and direct blending.\"\"\"\n n = len(sequence)\n sum_sequence = np.sum(sequence)\n if sum_sequence <= 0.0:\n return None\n conv = np.convolve(sequence, sequence)\n max_b_val = np.max(conv)\n threshold = 0.4 * max_b_val if max_b_val > 0 else 0.05\n tight_positions = np.where(conv >= threshold)[0]\n if not tight_positions.size:\n tight_positions = np.arange(2 * n - 1)\n g_fun = solve_convolution_lp(sequence, max_b_val, tight_positions)\n if g_fun is None:\n return None\n sum_g = np.sum(g_fun)\n if sum_g <= 0.0:\n return None\n # Golden-section search for optimal t\n def objective(t):\n new_seq = [ (1 - t) * x + t * y for x, y in zip(sequence, g_fun) ]\n return evaluate_sequence(new_seq)\n low, high = 0.0, 1.0\n for _ in range(100):\n t1 = low + (high - low) / 3\n t2 = high - (high - low) / 3\n f1 = objective(t1)\n f2 = objective(t2)\n if f1 < f2:\n high = t2\n else:\n low = t1\n best_t = (low + high) / 2\n new_seq = [ (1 - best_t) * x + best_t * y for x, y in zip(sequence, g_fun) ]\n return new_seq\n\n def solve_convolution_lp(f_sequence, rhs, tight_positions):\n n = len(f_sequence)\n if n == 0:\n return None\n g = cp.Variable(n, nonneg=True)\n objective = cp.Minimize(-cp.sum(g))\n constraints = []\n # Use all positions for constraints instead of subset\n for k in tight_positions:\n coeff = np.zeros(n)\n for j in range(n):\n i = k - j\n if 0 <= i < n:\n coeff[j] = f_sequence[i]\n constraints.append(cp.sum(coeff * g) <= rhs)\n problem = cp.Problem(objective, constraints)\n try:\n problem.solve(solver=cp.GLPK, verbose=False, eps=1e-6)\n return g.value if problem.status == cp.OPTIMAL else None\n except:\n return None\n\n def explore_neighborhood(sequence):\n \"\"\"Balance and refine based on key convolution positions.\"\"\"\n n = len(sequence)\n conv = np.convolve(sequence, sequence)\n top_indices = np.argsort(conv)[-10:]\n indices = [min(idx, n-1) for idx in top_indices]\n for idx in indices:\n if sequence[idx] > 0.0:\n sequence[idx] = max(0.0, sequence[idx] * 0.9)\n for shift in [-1, 0, 1]:\n neighbor_idx = idx + shift\n if 0 <= neighbor_idx < n:\n sequence[neighbor_idx] = min(1000.0, sequence[neighbor_idx] * 1.05)\n sequence = [max(0.0, min(1000.0, x)) for x in sequence]\n return sequence\n\n def expand_sequence(seq):\n \"\"\"Expand sequence by balancing the added element with existing ones.\"\"\"\n n = len(seq)\n if n >= 1500:\n return seq\n new_val = max(0.0, np.mean(seq) * 0.6) # Smaller new element to avoid overload\n new_seq = seq + [new_val]\n new_sum = np.sum(new_seq)\n # Scale sum to prevent it from being too small\n if new_sum < 0.01:\n scale_factor = 0.01 / new_sum\n new_seq = [x * scale_factor for x in new_seq]\n return new_seq\n\n while time.time() < deadline:\n # Try LP-based move\n h_function = get_good_direction_to_move_into(current_sequence)\n if h_function is not None:\n current_sequence = h_function\n else:\n # Expand if not already at limit\n if len(current_sequence) < 1500:\n current_sequence = expand_sequence(current_sequence)\n else:\n current_sequence = perturb_sequence(current_sequence)\n\n # Occasionally try random walk for exploration\n if np.random.rand() < 0.05:\n current_sequence = random_walk(current_sequence)\n\n # Try structured balancing on key positions\n if np.random.rand() < 0.03:\n seq = [max(0.0, x) for x in current_sequence]\n n = len(seq)\n conv = np.convolve(seq, seq)\n peak_indices = np.argsort(conv)[-10:] # Focus on important peak positions\n peak_indices = np.array([min(idx, n-1) for idx in peak_indices])\n for idx in peak_indices:\n if seq[idx] > 0.0:\n seq[idx] = max(0.0, seq[idx] * 0.8)\n for shift in [-1, 0, 1]:\n neighbor_idx = idx + shift\n if 0 <= neighbor_idx < n:\n seq[neighbor_idx] = min(1000.0, seq[neighbor_idx] * 1.05)\n current_sequence = [max(0.0, min(1000.0, x)) for x in seq]\n\n # Explore neighborhood\n if np.random.rand() < 0.02:\n current_sequence = explore_neighborhood(current_sequence)\n\n try:\n curr_score = evaluate_sequence(current_sequence)\n if curr_score < best_score:\n best_score = curr_score\n best_sequence = current_sequence.copy()\n print(f\"New best: {best_score}\")\n except Exception:\n pass\n\n return [float(max(0.0, x)) for x in best_sequence]\n```",
64 "env/all/time/policy": 539.9991992157884,
65 "env/all/time/policy/min": 275.7938234806061,
66 "env/all/time/policy/max": 767.7666115760803,
67 "env/all/time/env_step": 2943.3939134194516,
68 "env/all/time/env_step/min": 0.857710599899292,
69 "env/all/time/env_step/max": 5627.398071527481,
70 "env/all/time/reward_compute": 9.41101461648941e-07,
71 "env/all/time/reward_compute/min": 2.4959444999694824e-07,
72 "env/all/time/reward_compute/max": 2.7194619178771973e-06,
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": 0.020513620227575302,
77 "advantage/min": -1.0,
78 "advantage/max": 13.376224517822266,
79 "time/assemble_training_data": 5.8522560596466064,
80 "time/kl_vs_base": 149.7440550327301,
81 "kl_policy_base": 0.0009914402617141604,
82 "time/train": 1171.0154283046722,
83 "time/save_checkpoint": 19.061925649642944,
84 "time/total": 7742.452511310577
85}[2026-07-09T06:24:18+00:00] job=1812630 node=node-14 ngpu=3 ntrain=1 replicas=2 flash_attn=no
[2026-07-09T09:14:17+00:00] job=1813129 node=node-28 ngpu=3 ntrain=1 replicas=2 flash_attn=yes
[2026-07-09T13:46:36+00:00] job=1813130 node=node-22 ngpu=6 ntrain=2 replicas=4 flash_attn=yes
[2026-07-11T14:04:05+00:00] job=1824338 node=node-1 ngpu=3 ntrain=1 replicas=2 flash_attn=yes