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"""
Convert RL test trajectories (numeric states/actions) from FrozenLake into
LLM SFT-ready language trajectories in chat-style messages.
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 RAGEN ContextManager’s prompt format as much as possible:
- system: "You're a helpful assistant. "
- user: env_instruction + per-turn state blocks with action constraints
- assistant: "<think></think><answer>Action</answer>" (or without think if disabled)
- user (reward): "Reward:\n{reward}\n"
"""
import argparse
import json
import math
import os
from pathlib import Path
from typing import List, Tuple
try:
import yaml # type: ignore
except Exception:
yaml = None
ACTION_LOOKUP = {1: "Left", 2: "Down", 3: "Right", 4: "Up"}
def infer_grid_dims(state_vec: List[float]) -> Tuple[int, int]:
"""Infer (rows, cols) from flattened one-hot grid length.
Our PPO wrapper encodes each cell as one-hot over 6 tokens: ['P','_','O','G','X','√'].
"""
n = len(state_vec)
assert n % 6 == 0, f"State length {n} not divisible by 6 (channels)"
n_cells = n // 6
r = int(math.isqrt(n_cells))
assert r * r == n_cells, f"Grid is not square: {n_cells} cells"
return r, r
def decode_state_to_grid_text(state_vec: List[float]) -> str:
"""Decode numeric state vector back to textual grid.
Encoding per PPO wrapper:
One-hot per cell over tokens = ['P', '_', 'O', 'G', 'X', '√'] in this order.
The wrapper already encodes P/X/√ directly in the grid; no separate coords needed.
"""
tokens = ['P', '_', 'O', 'G', 'X', '√']
rows, cols = infer_grid_dims(state_vec)
lines = []
for i in range(rows):
row_chars = []
for j in range(cols):
base = (i * cols + j) * 6
cell = state_vec[base: base + 6]
idx = max(range(6), key=lambda k: cell[k])
ch = tokens[idx] if 0 <= idx < len(tokens) else '_'
row_chars.append(ch)
lines.append("".join(row_chars))
return "\n".join(lines)
def load_env_instruction_and_cfg(repo_root: Path) -> Tuple[str, int, str, bool]:
"""Load FrozenLake env_instruction, max_tokens, action_sep, enable_think from config.
Fallbacks are provided if YAML is unavailable.
"""
default_instruction = (
"You are solving the FrozenLake puzzle. Forbid the hole and go to the target. "
"You may move to unintended directions due to slippery ice. "
"Example answer format: <think>To forbid the hole and go to the target, I should go left then go up.</think><answer>Left || Up</answer>"
"The meaning of each symbol in the state is:\nP: player, _: empty, O: hole, G: goal, X: player in hole, √: player on goal \nYour available actions are: \nLeft, Down, Right, Up \nYou can make up to 10 actions, separated by the action separator ' || '"
)
instruction = default_instruction
max_tokens = 100
action_sep = "||"
enable_think = True
if yaml is None:
return instruction, max_tokens, action_sep, enable_think
# envs.yaml
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 "FrozenLake" in envs:
fl = envs["FrozenLake"]
instruction = fl.get("env_instruction", instruction)
max_tokens = int(fl.get("max_tokens", max_tokens))
except Exception:
pass
# base.yaml
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
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,
) -> List[dict]:
"""Construct chat messages mirroring ContextManager format.
- First system message.
- One user message containing the instruction and per-turn state blocks.
- Assistant messages per executed action with tag-only outputs.
- User messages for rewards.
"""
messages = [
{"role": "system", "content": "You're a helpful assistant. "},
{"role": "user", "content": instruction},
]
total_actions = len(actions)
# Append state blocks into the initial user content
for t, state in enumerate(states):
grid_text = decode_state_to_grid_text(state)
actions_left = max(0, total_actions - t) # before taking action at turn 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{grid_text}\n"
f"You have {actions_left} actions left. Always output: {format_prompt} "
f"with no extra text. Strictly follow this format. {length_prompt}\n"
)
# If action exists for this turn, add assistant + reward
if t < total_actions:
# Map RL action (0..3) -> RAGEN action (1..4) -> text
action_id = actions[t] + 1
action_name = ACTION_LOOKUP.get(action_id, "unknown")
if enable_think:
assistant_text = f"<think></think><answer>{action_name}</answer>"
else:
assistant_text = f"<answer>{action_name}</answer>"
messages.append({"role": "assistant", "content": assistant_text})
# Reward message
reward_val = rewards[t] if t < len(rewards) else 0.0
messages.append({"role": "user", "content": f"Reward:\n{reward_val}\n"})
# import pdb;pdb.set_trace()
messages.append({"role": "assistant", "content": "<think>"})
return messages
def convert_file(step_dir: Path, output_dir: Path, repo_root: Path, include_failed: bool = False) -> 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 = load_env_instruction_and_cfg(repo_root)
output_dir.mkdir(parents=True, exist_ok=True)
out_path = output_dir / f"{step_dir.name}_sft.jsonl"
# Read global step from metrics if available
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)
# Filter if requested
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", [])
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,
)
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:
# Still write an empty file to signal conversion executed
with open(out_path, "w", encoding="utf-8") as f:
pass
return out_path
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}")
# Sort by numeric suffix
step_dirs.sort(key=lambda p: int(p.name.split("_")[-1]))
return step_dirs[-1]
def main():
parser = argparse.ArgumentParser(description="Convert FrozenLake 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_993280)")
parser.add_argument("--include_failed", action="store_true", help="Include failed episodes in SFT data")
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)
print(f"SFT data written to: {out_path}")
if __name__ == "__main__":
main()
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