| |
| """ |
| Convert RL eval trajectories from Rubik's Cube 2x2 into LLM SFT-ready chat data. |
| |
| Input: runs/<exp>/trajectories/step_XXXXXX/trajectories.jsonl |
| Output: runs/<exp>/sft/step_XXXXXX_sft.jsonl |
| |
| Each output JSON line contains: |
| - messages: [{role: system|user|assistant, content: str}, ...] |
| - meta: {episode_return: float, episode_success: bool, global_step: int} |
| |
| We mirror the FrozenLake converter structure: |
| - system: "You're a helpful assistant. " |
| - user: env_instruction + per-turn state blocks |
| - assistant: tag-only actions, one per step |
| - user: reward after each action |
| """ |
|
|
| import argparse |
| import json |
| from pathlib import Path |
| from typing import List, Tuple |
|
|
| try: |
| import yaml |
| except Exception: |
| yaml = None |
|
|
|
|
| |
| RUBIK_ACTIONS = [ |
| "U", "U'", "D", "D'", "L", "L'", "R", "R'", "F", "F'", "B", "B'", |
| ] |
|
|
| COLORS = ['W', 'O', 'G', 'R', 'B', 'Y'] |
|
|
|
|
| def load_env_instruction_and_cfg(repo_root: Path) -> Tuple[str, int, str, bool, int]: |
| """Load RubiksCube2x2 env instruction and base agent_proxy configs. |
| Returns: (instruction, max_tokens, action_sep, enable_think, max_actions) |
| """ |
| instruction = ( |
| "You are solving a 2x2 Rubik's Cube (Pocket Cube). The goal is to restore the cube so that each of the faces consists of a single, unique color.\n" |
| "Available actions use standard Singmaster notation for face rotations: U, U', D, D', L, L', R, R', F, F', B, B'.\n" |
| "- Faces: U (Up), D (Down), L (Left), R (Right), F (Front), B (Back).\n" |
| "- Modifiers: A letter alone means 90° clockwise (e.g., 'R'). A letter with prime (') means 90° counter-clockwise (e.g., \"R'\")." |
| "Respond with a sequence of actions separated by \"||\".\n" |
| "Example: <answer>U</answer>\n\n" |
| "Your available actions are:\n" |
| "U, U', D, D', L, L', R, R', F, F', B, B'\n" |
| "You can make up to 20 actions, separated by the action separator \" || \"\n" |
| ) |
| max_tokens = 96 |
| action_sep = "||" |
| enable_think = True |
| max_actions = 20 |
|
|
| if yaml is None: |
| return instruction, max_tokens, action_sep, enable_think, max_actions |
|
|
| |
| envs_yaml = repo_root / "config" / "envs.yaml" |
| if envs_yaml.exists(): |
| try: |
| with open(envs_yaml, "r", encoding="utf-8") as f: |
| envs = yaml.safe_load(f) |
| if isinstance(envs, dict) and "custom_envs" in envs and "RubiksCube2x2" in envs["custom_envs"]: |
| e = envs["custom_envs"]["RubiksCube2x2"] |
| instruction = e.get("env_instruction", instruction) |
| max_tokens = int(e.get("max_tokens", max_tokens)) |
| max_actions = int(e.get("max_actions_per_traj", max_actions)) |
| except Exception: |
| pass |
|
|
| |
| base_yaml = repo_root / "config" / "base.yaml" |
| if base_yaml.exists(): |
| try: |
| with open(base_yaml, "r", encoding="utf-8") as f: |
| base_cfg = yaml.safe_load(f) |
| ap = base_cfg.get("agent_proxy", {}) if isinstance(base_cfg, dict) else {} |
| action_sep = ap.get("action_sep", action_sep) |
| enable_think = bool(ap.get("enable_think", enable_think)) |
| except Exception: |
| pass |
|
|
| return instruction, max_tokens, action_sep, enable_think, max_actions |
|
|
|
|
| def decode_state_to_text(state_vec: List[float]) -> str: |
| """Decode one-hot length 24*6 vector into sticker letters. |
| Returns a compact textual block listing each face in order: U, L, F, R, B, D. |
| """ |
| if not state_vec: |
| return "" |
| n = len(state_vec) |
| if n % len(COLORS) != 0: |
| return "" |
| n_stickers = n // len(COLORS) |
| if n_stickers != 24: |
| |
| pass |
| |
| stickers: List[str] = [] |
| for i in range(n_stickers): |
| base = i * len(COLORS) |
| cell = state_vec[base: base + len(COLORS)] |
| idx = max(range(len(COLORS)), key=lambda k: cell[k]) |
| c = COLORS[idx] if 0 <= idx < len(COLORS) else '?' |
| stickers.append(c) |
| |
| faces = [stickers[i*4:(i+1)*4] for i in range(6)] |
| face_names = ["Up (U)", "Left (L)", "Front (F)", "Right (R)", "Back (B)", "Down (D)"] |
| lines = ["=== Rubik's Cube 2x2 State ===\n"] |
| for name, face in zip(face_names, faces): |
| lines.append(f"{name}: [{face[0]}, {face[1]}] \n [{face[2]}, {face[3]}]") |
| lines.append("\nAvailable actions: \n" + ", ".join(RUBIK_ACTIONS)) |
| return "\n".join(lines) |
|
|
|
|
| def build_messages_for_episode( |
| states: List[List[float]], |
| actions: List[int], |
| rewards: List[float], |
| instruction: str, |
| max_tokens: int, |
| action_sep: str, |
| enable_think: bool, |
| max_actions: int, |
| ) -> List[dict]: |
| messages = [ |
| {"role": "system", "content": "You're a helpful assistant. "}, |
| {"role": "user", "content": instruction}, |
| ] |
|
|
| total_actions = len(actions) |
| for t, state in enumerate(states): |
| state_text = decode_state_to_text(state) |
| actions_left = max(0, max_actions - t) |
| format_prompt = ( |
| "<think> [Your thoughts] </think> <answer> [your answer] </answer>" |
| if enable_think |
| else "<answer> [your answer] </answer>" |
| ) |
| length_prompt = f"Max response length: {max_tokens} words (tokens)." |
|
|
| messages[-1]["content"] += ( |
| f"\nTurn {t + 1}:\n" |
| f"State:\n{state_text}\n" |
| f"\nWhat is your next move?\n" |
| f"You have {actions_left} actions left. Always output: {format_prompt}" |
| f"with no extra text. {length_prompt}" |
| ) |
|
|
| if t < total_actions: |
| a = actions[t] |
| a_name = RUBIK_ACTIONS[int(a)] if 0 <= int(a) < len(RUBIK_ACTIONS) else str(a) |
| if enable_think: |
| assistant_text = f"<think> </think><answer>{a_name}</answer>" |
| else: |
| assistant_text = f"<answer>{a_name}</answer>" |
| messages.append({"role": "assistant", "content": assistant_text}) |
| r = rewards[t] if t < len(rewards) else 0.0 |
| messages.append({"role": "user", "content": f"Reward:\n{r}\n"}) |
| |
| return messages[:-1] |
|
|
|
|
| def find_latest_step_dir(traj_root: Path) -> Path: |
| step_dirs = [p for p in traj_root.iterdir() if p.is_dir() and p.name.startswith("step_")] |
| if not step_dirs: |
| raise FileNotFoundError(f"No step_* directories under {traj_root}") |
| step_dirs.sort(key=lambda p: int(p.name.split("_")[-1])) |
| return step_dirs[-1] |
|
|
|
|
| def convert_file(step_dir: Path, output_dir: Path, repo_root: Path, include_failed: bool, max_actions_cap: int | None) -> Path: |
| traj_path = step_dir / "trajectories.jsonl" |
| metrics_path = step_dir / "metrics.json" |
| if not traj_path.exists(): |
| raise FileNotFoundError(f"Missing trajectories.jsonl at {traj_path}") |
|
|
| instruction, max_tokens, action_sep, enable_think, default_max_actions = load_env_instruction_and_cfg(repo_root) |
| max_actions = int(max_actions_cap) if max_actions_cap is not None else int(default_max_actions) |
|
|
| output_dir.mkdir(parents=True, exist_ok=True) |
| out_path = output_dir / f"{step_dir.name}_sft.jsonl" |
|
|
| |
| global_step = None |
| if metrics_path.exists(): |
| try: |
| with open(metrics_path, "r", encoding="utf-8") as f: |
| m = json.load(f) |
| global_step = m.get("global_step") |
| except Exception: |
| pass |
|
|
| written = 0 |
| with open(traj_path, "r", encoding="utf-8") as fin, open(out_path, "w", encoding="utf-8") as fout: |
| for line in fin: |
| line = line.strip() |
| if not line: |
| continue |
| traj = json.loads(line) |
|
|
| ep_success = bool(traj.get("episode_success", False)) |
| if (not include_failed) and (not ep_success): |
| continue |
|
|
| states = traj.get("states", []) |
| actions = traj.get("actions", []) |
| rewards = traj.get("rewards", []) |
| if len(actions) > max_actions: |
| continue |
|
|
| messages = build_messages_for_episode( |
| states=states, |
| actions=actions, |
| rewards=rewards, |
| instruction=instruction, |
| max_tokens=max_tokens, |
| action_sep=action_sep, |
| enable_think=enable_think, |
| max_actions=max_actions, |
| ) |
|
|
| record = { |
| "messages": messages, |
| "meta": { |
| "episode_return": traj.get("episode_return", None), |
| "episode_success": ep_success, |
| "global_step": global_step, |
| }, |
| } |
| fout.write(json.dumps(record, ensure_ascii=False) + "\n") |
| written += 1 |
|
|
| if written == 0: |
| with open(out_path, "w", encoding="utf-8"): |
| pass |
| return out_path |
|
|
|
|
| def main(): |
| parser = argparse.ArgumentParser(description="Convert Rubik's Cube 2x2 RL trajectories to LLM SFT chat JSONL") |
| parser.add_argument("run_dir", help="Path to the run directory (contains trajectories/)") |
| parser.add_argument("--step", default=None, help="Specific step directory name (e.g., step_123456)") |
| parser.add_argument("--include_failed", action="store_true", help="Include failed episodes in SFT data") |
| parser.add_argument("--max_actions", type=int, default=None, help="Override max actions cap (default from envs.yaml)") |
| args = parser.parse_args() |
|
|
| repo_root = Path(__file__).resolve().parents[1] |
| run_dir = Path(args.run_dir) |
| traj_root = run_dir / "trajectories" |
| if not traj_root.exists(): |
| raise FileNotFoundError(f"Not found trajectories directory: {traj_root}") |
|
|
| step_dir = traj_root / args.step if args.step else find_latest_step_dir(traj_root) |
| output_dir = run_dir / "sft" |
| out_path = convert_file(step_dir=step_dir, output_dir=output_dir, repo_root=repo_root, include_failed=args.include_failed, max_actions_cap=args.max_actions) |
| print(f"SFT data written to: {out_path}") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|