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erdos. Checkpoint saved
after training step 29 (0-indexed). Strict upstream eval parity:
1100s hard kill, verbatim prompts/entrypoints, group 64x8, T=1.0, kl 0.1.1{
2 "step": 29,
3 "progress/batch": 29,
4 "optim/lr": 4e-05,
5 "progress/done_frac": 0.6,
6 "puct/buffer_size": 472,
7 "puct/sampled_size": 8,
8 "puct/T": 14848,
9 "puct/scale_last": 0.11907473987989126,
10 "puct/buffer_value/mean": -0.38403368380778913,
11 "puct/buffer_value/std": 0.017280398017336572,
12 "puct/buffer_value/min": -0.5130522804051018,
13 "puct/buffer_value/max": -0.38092526012010874,
14 "puct/buffer_timestep/mean": 13.745762711864407,
15 "puct/buffer_timestep/std": 8.518361464823458,
16 "puct/buffer_timestep/min": -1.0,
17 "puct/buffer_timestep/max": 28.0,
18 "puct/buffer_construction_len/mean": 92.27966101694915,
19 "puct/buffer_construction_len/std": 34.33789861422831,
20 "puct/buffer_construction_len/min": 40.0,
21 "puct/buffer_construction_len/max": 200.0,
22 "puct/sampled_value/mean": -0.38092526841943763,
23 "puct/sampled_value/std": 1.1762830312647338e-09,
24 "puct/sampled_value/min": -0.3809252708304497,
25 "puct/sampled_value/max": -0.38092526724844983,
26 "puct/sampled_timestep/mean": 28.0,
27 "puct/sampled_timestep/std": 0.0,
28 "puct/sampled_timestep/min": 28.0,
29 "puct/sampled_timestep/max": 28.0,
30 "puct/sampled_construction_len/mean": 125.0,
31 "puct/sampled_construction_len/std": 43.30127018922193,
32 "puct/sampled_construction_len/min": 100.0,
33 "puct/sampled_construction_len/max": 200.0,
34 "time/sampling": 1856.4106550216675,
35 "env/all/ac_tokens_per_turn": 9353.798828125,
36 "env/all/ob_tokens_per_turn": 1730.125,
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": 4789145,
41 "env/all/total_ob_tokens": 885824,
42 "env/all/time/sampling_mean": 530.8880034275353,
43 "env/all/time/sampling_max": 792.6746079921722,
44 "env/all/time/env_step_mean": 57.15611411770806,
45 "env/all/time/env_step_max": 1100.1232821941376,
46 "env/all/reward/mean": 1.0356710448525601,
47 "env/all/reward/max": 2.625186786965431,
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": 1.0356710448525601,
53 "env/all/correctness": 0.41015625,
54 "env/all/correctness/min": 0.0,
55 "env/all/correctness/max": 1.0,
56 "env/all/raw_score": 0.40177312459087416,
57 "env/all/raw_score/min": 0.3809252654757097,
58 "env/all/raw_score/max": 1.0,
59 "env/all/initial_raw_score": -0.38092526841943763,
60 "env/all/initial_raw_score/min": -0.3809252708304497,
61 "env/all/initial_raw_score/max": -0.38092526724844983,
62 "env/all/msg": "Success; raw_score=0.38092526684338396",
63 "env/all/parsed_code": "```python\nimport numpy as np\nfrom scipy.optimize import minimize\n\ndef run(seed=42, budget_s=1000, **kwargs):\n # Use fewer points for speed\n n_points = 100\n dx = 2.0 / n_points\n np.random.seed(seed)\n\n # Desired sum of h values to match \u222b\u2080\u00b2 h(x) dx = 1\n desired_sum = n_points / 2\n\n # Evaluate the maximum overlap integral\n def evaluate_erdos(h):\n corr = np.correlate(h, 1 - h, mode=\"full\")\n return np.max(corr) * dx\n\n # Create a structured initial guess with evenly spaced intervals\n def create_evenly_spaced_initial(n_points=100):\n h = np.zeros(n_points)\n h[0:17] = 1.0 # First interval\n h[50:67] = 1.0 # Second interval\n h[82:98] = 1.0 # Third interval\n h = np.clip(h, 0.0, 1.0)\n sum_h = np.sum(h)\n delta = desired_sum - sum_h\n if abs(delta) > 1e-6:\n h[-1] += delta\n h = np.clip(h, 0.0, 1.0)\n return h\n\n # Generate structured initial guesses\n structured_initials = [\n np.zeros(n_points),\n np.zeros(n_points),\n np.zeros(n_points)\n ]\n # First guess: high in first half\n initial1 = np.zeros(n_points)\n initial1[:n_points//2] = 1.0\n initial1[-1] += (desired_sum - np.sum(initial1))\n initial1 = np.clip(initial1, 0.0, 1.0)\n structured_initials[0] = initial1.copy()\n\n # Second guess: two intervals\n initial2 = np.zeros(n_points)\n initial2[:n_points//4] = 1.0\n initial2[-n_points//4:] = 1.0\n initial2[-1] += (desired_sum - np.sum(initial2))\n initial2 = np.clip(initial2, 0.0, 1.0)\n structured_initials[1] = initial2.copy()\n\n # Third guess: alternating high and low\n initial3 = np.zeros(n_points)\n for j in range(n_points):\n if j % 2 == 0:\n initial3[j] = 1.0\n initial3[-1] += (desired_sum - np.sum(initial3))\n initial3 = np.clip(initial3, 0.0, 1.0)\n structured_initials[2] = initial3.copy()\n\n # Fourth guess: structured even spacing\n structured_initials.append(create_evenly_spaced_initial())\n\n # Initialize population\n population_size = 500\n population = []\n\n # Add initial_h_values if provided (from problem setup)\n initial_h = initial_h_values.copy()\n initial_h = np.array(initial_h, dtype=np.float64)\n initial_h = np.clip(initial_h, 0.0, 1.0)\n total = np.sum(initial_h)\n delta = desired_sum - total\n initial_h[-1] += delta\n initial_h = np.clip(initial_h, 0.0, 1.0)\n population.append(initial_h.copy())\n\n # Add structured initial guesses\n for h in structured_initials:\n population.append(h.copy())\n\n # Add some random initial guesses\n for _ in range(population_size - len(population)):\n h = np.random.uniform(0.0, 1.0, n_points)\n total = np.sum(h)\n delta = desired_sum - total\n h[-1] += delta\n h = np.clip(h, 0.0, 1.0)\n population.append(h.copy())\n\n # Genetic algorithm parameters\n generations = 1000\n elite_ratio = 0.1\n mutation_rate = 0.3\n\n best_c5 = float('inf')\n best_h = None\n\n # Run the genetic algorithm\n for gen in range(generations):\n # Evaluate each candidate\n fitness = [evaluate_erdos(h) for h in population]\n best_idx = np.argmin(fitness)\n\n # Update best solution\n if fitness[best_idx] < best_c5:\n best_c5 = fitness[best_idx]\n best_h = population[best_idx].copy()\n\n # Select elites\n elite_count = int(elite_ratio * population_size)\n elite_indices = np.argsort(fitness)[:elite_count]\n elites = [population[i].copy() for i in elite_indices]\n\n # Generate next population\n new_population = elites.copy()\n while len(new_population) < population_size:\n # Crossover\n parent1 = population[np.random.choice(elite_count)]\n parent2 = population[np.random.choice(elite_count)]\n crossover = np.random.rand()\n child = crossover * parent1 + (1 - crossover) * parent2\n\n # Mutation\n if np.random.rand() < mutation_rate:\n idx = np.random.randint(n_points)\n delta = np.random.uniform(-0.07, 0.07) * child[idx]\n child[idx] += delta\n other_idx = np.random.randint(n_points)\n child[other_idx] -= delta\n child = np.clip(child, 0.0, 1.0)\n\n new_population.append(child.copy())\n\n population = new_population\n\n # Local optimize the best individual\n if best_h is not None:\n h_array = np.array(best_h, dtype=np.float64)\n corr = np.correlate(h_array, 1 - h_array, mode=\"full\")\n max_c5 = np.max(corr) * dx\n\n def objective(h):\n h_array = np.array(h, dtype=np.float64)\n corr = np.correlate(h_array, 1 - h_array, mode=\"full\")\n return np.max(corr) * dx\n\n bounds = [(0.0, 1.0) for _ in range(n_points)]\n constraint_func = lambda h: np.sum(h) - desired_sum\n\n res = minimize(\n fun=objective,\n x0=best_h,\n method='SLSQP',\n bounds=bounds,\n constraints=[{'type': 'eq', 'fun': constraint_func}],\n options={\n 'ftol': 1e-10,\n 'maxiter': 500,\n 'disp': False\n }\n )\n\n if res.success:\n new_c5 = np.max(np.correlate(res.x, 1 - res.x, mode=\"full\") * dx)\n if new_c5 < best_c5:\n best_c5 = new_c5\n best_h = res.x\n\n return (best_h, best_c5, n_points)\n```",
64 "env/all/time/policy": 530.8880034275353,
65 "env/all/time/policy/min": 253.8745527267456,
66 "env/all/time/policy/max": 792.6746079921722,
67 "env/all/time/env_step": 57.15611411770806,
68 "env/all/time/env_step/min": 0.006021261215209961,
69 "env/all/time/env_step/max": 1100.1232821941376,
70 "env/all/time/reward_compute": 4.4796615839004517e-07,
71 "env/all/time/reward_compute/min": 2.4586915969848633e-07,
72 "env/all/time/reward_compute/max": 1.1846423149108887e-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": 0.02228579670190811,
77 "advantage/min": -1.0,
78 "advantage/max": 3.026306629180908,
79 "time/assemble_training_data": 6.383984804153442,
80 "time/kl_vs_base": 134.53201532363892,
81 "kl_policy_base": 0.0008650023373775184,
82 "time/train": 1036.4237716197968,
83 "time/save_checkpoint": 17.30647897720337,
84 "time/total": 3053.0280709266663
85}[2026-07-09T06:24:18+00:00] job=1812626 node=node-31 ngpu=3 ntrain=1 replicas=2 flash_attn=no
[2026-07-09T08:02:36+00:00] job=1813125 node=node-12 ngpu=3 ntrain=1 replicas=2 flash_attn=yes
[2026-07-09T09:32:13+00:00] job=1813126 node=node-3 ngpu=6 ntrain=2 replicas=4 flash_attn=yes
[2026-07-09T10:04:31+00:00] job=1813623 node=node-31 ngpu=3 ntrain=1 replicas=2 flash_attn=yes