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640cca9 5b9b298 640cca9 5b9b298 640cca9 5b9b298 640cca9 5b9b298 640cca9 5b9b298 640cca9 5b9b298 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 | """
Theme-4 training starter for SmartPayEnv.
This script demonstrates a novel self-improvement loop:
1) sample K candidate actions per observation
2) score each candidate with /simulate rewards (group-relative signal)
3) collect best/worst pairs for preference-style post-training
It is intentionally lightweight so teams can run it in Colab with TRL/Unsloth.
"""
from __future__ import annotations
import argparse
import json
import os
import random
from dataclasses import dataclass
from typing import Any
import requests
ENV_URL = os.getenv("ENV_URL", "http://localhost:7860").rstrip("/")
MAX_STEPS = int(os.getenv("MAX_STEPS", "200"))
GROUP_SIZE = int(os.getenv("GROUP_SIZE", "8"))
DIFFICULTY = int(os.getenv("DIFFICULTY", "2"))
RANDOM_SEED = int(os.getenv("SEED", "42"))
@dataclass
class RolloutExample:
prompt: str
chosen: str
rejected: str
chosen_reward: float
rejected_reward: float
def _action_candidates() -> list[dict[str, int]]:
all_actions: list[dict[str, int]] = []
for gateway in (0, 1, 2):
for fraud_decision in (0, 1, 2, 3):
for retry_strategy in (0, 1):
all_actions.append(
{
"gateway": gateway,
"fraud_decision": fraud_decision,
"retry_strategy": retry_strategy,
}
)
random.shuffle(all_actions)
return all_actions
def _simulate_reward(action: dict[str, int]) -> float:
response = requests.post(f"{ENV_URL}/simulate", json={"action": action}, timeout=30)
response.raise_for_status()
obs = response.json()
return float(obs.get("reward", 0.0))
def _step(action: dict[str, int]) -> dict[str, Any]:
response = requests.post(f"{ENV_URL}/step", json={"action": action}, timeout=30)
response.raise_for_status()
return response.json()
def _reset(difficulty: int = 2) -> dict[str, Any]:
response = requests.post(f"{ENV_URL}/reset", json={"difficulty": difficulty}, timeout=30)
response.raise_for_status()
payload = response.json()
return payload.get("observation", payload)
def collect_group_relative_pairs(
max_steps: int = MAX_STEPS,
group_size: int = GROUP_SIZE,
difficulty: int = DIFFICULTY,
seed: int = RANDOM_SEED,
) -> list[RolloutExample]:
random.seed(seed)
obs = _reset(difficulty=difficulty)
dataset: list[RolloutExample] = []
actions_pool = _action_candidates()
for _ in range(max_steps):
sampled = random.sample(actions_pool, k=min(group_size, len(actions_pool)))
scored: list[tuple[dict[str, int], float]] = []
for action in sampled:
try:
reward = _simulate_reward(action)
scored.append((action, reward))
except requests.RequestException:
continue
if len(scored) < 2:
break
scored.sort(key=lambda x: x[1], reverse=True)
best_action, best_reward = scored[0]
worst_action, worst_reward = scored[-1]
prompt = (
"SmartPayEnv observation:\n"
f"{json.dumps(obs, sort_keys=True)}\n"
"Return one action JSON with fields: gateway, fraud_decision, retry_strategy."
)
dataset.append(
RolloutExample(
prompt=prompt,
chosen=json.dumps(best_action, sort_keys=True),
rejected=json.dumps(worst_action, sort_keys=True),
chosen_reward=best_reward,
rejected_reward=worst_reward,
)
)
step_payload = _step(best_action)
obs = step_payload.get("observation", step_payload)
if bool(obs.get("done", False)):
obs = _reset(difficulty=difficulty)
return dataset
def export_jsonl(dataset: list[RolloutExample], output_path: str) -> None:
with open(output_path, "w", encoding="utf-8") as f:
for row in dataset:
f.write(
json.dumps(
{
"prompt": row.prompt,
"chosen": row.chosen,
"rejected": row.rejected,
"chosen_reward": row.chosen_reward,
"rejected_reward": row.rejected_reward,
}
)
+ "\n"
)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Collect group-relative preference pairs from SmartPayEnv.")
parser.add_argument("--env-url", default=ENV_URL, help="SmartPayEnv server URL")
parser.add_argument("--max-steps", type=int, default=MAX_STEPS, help="Number of rollout steps")
parser.add_argument("--group-size", type=int, default=GROUP_SIZE, help="Actions sampled per step")
parser.add_argument("--difficulty", type=int, default=DIFFICULTY, help="Environment difficulty 0/1/2")
parser.add_argument("--seed", type=int, default=RANDOM_SEED, help="Random seed")
parser.add_argument("--output", default="theme4_grpo_pairs.jsonl", help="Output JSONL path")
args = parser.parse_args()
ENV_URL = args.env_url.rstrip("/")
data = collect_group_relative_pairs(
max_steps=args.max_steps,
group_size=args.group_size,
difficulty=args.difficulty,
seed=args.seed,
)
export_jsonl(data, args.output)
print(f"Collected {len(data)} preference pairs into {args.output}")
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