training/training_script.py

"""GRPO (Group-Relative Policy Optimization) training script for CERNenv.



Uses Hugging Face TRL (Transformer Reinforcement Learning) ``GRPOTrainer`` to

fine-tune a small instruction-tuned model on full episodes of the CERN

environment. Each ``query`` is a prompt sampled from a freshly-reset env;

the reward function rolls the model's response through the environment and

returns the per-step + (optional) terminal reward.



This script is intentionally CPU-friendly and self-contained. For

GPU-accelerated training with LoRA, prefer ``training_unsloth.py``.



Run:

    python -m training.training_script \

        --model_name HuggingFaceTB/SmolLM2-360M-Instruct \

        --total_episodes 200 --max_steps 18 --output_dir training/grpo-output

"""

from __future__ import annotations

import argparse
import logging
import math
import os
from dataclasses import dataclass
from typing import Any, Dict, List, Optional

import torch
from datasets import Dataset
from transformers import AutoModelForCausalLM, AutoTokenizer

from models import ExperimentAction
from server.environment import CERNCollisionEnvironment
from training.llm_agent import (
    LLMAgentConfig,
    build_chat,
    parse_action,
    safe_default_action,
)


logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
logger = logging.getLogger(__name__)


# ── Episode reward harness ───────────────────────────────────────────────


@dataclass
class EpisodeContext:
    """Per-prompt reusable env + observation snapshot used by the reward fn."""

    env: CERNCollisionEnvironment
    seed: int
    scenario: Optional[str]
    difficulty: Optional[str]


def _stepwise_reward(

    *,

    completion_text: str,

    ctx: EpisodeContext,

) -> float:
    """Roll the model's first response through one full episode and

    return the cumulative reward (per-step + terminal).



    The completion is interpreted as the first action only; subsequent

    steps fall back to the safe default policy. This keeps the reward

    bandwidth high for early-exploration training without requiring

    multi-turn rollouts inside GRPO.

    """
    env = ctx.env
    obs = env.reset(seed=ctx.seed, scenario=ctx.scenario, difficulty=ctx.difficulty)

    action = parse_action(completion_text) or safe_default_action(obs)
    obs = env.step(action)
    cumulative = float(obs.reward or 0.0)

    while not obs.done:
        fallback = safe_default_action(obs)
        obs = env.step(fallback)
        cumulative += float(obs.reward or 0.0)

    return cumulative


def _format_validity_bonus(completion_text: str) -> float:
    return 0.5 if parse_action(completion_text) is not None else -0.5


def make_reward_fn(ctx: EpisodeContext):
    """Return a TRL-compatible reward function (closes over ``ctx``)."""

    def reward_fn(prompts: List[str], completions: List[str], **kwargs: Any) -> List[float]:
        rewards: List[float] = []
        for completion in completions:
            r = _stepwise_reward(completion_text=completion, ctx=ctx)
            r += _format_validity_bonus(completion)
            rewards.append(float(r))
        return rewards

    return reward_fn


# ── Prompt dataset ───────────────────────────────────────────────────────


def build_dataset(

    *,

    tokenizer,

    n_prompts: int,

    seed: int,

    scenario: Optional[str],

    difficulty: Optional[str],

) -> Dataset:
    env = CERNCollisionEnvironment()
    prompts: List[str] = []
    for i in range(n_prompts):
        obs = env.reset(seed=seed + i, scenario=scenario, difficulty=difficulty)
        chat = build_chat(obs)
        prompt = tokenizer.apply_chat_template(
            chat, add_generation_prompt=True, tokenize=False
        )
        prompts.append(prompt)
    return Dataset.from_dict({"prompt": prompts})


# ── Main ─────────────────────────────────────────────────────────────────


def main() -> None:  # pragma: no cover - training entrypoint
    parser = argparse.ArgumentParser()
    parser.add_argument("--model_name", default="HuggingFaceTB/SmolLM2-360M-Instruct")
    parser.add_argument("--scenario", default=None)
    parser.add_argument("--difficulty", choices=["easy", "medium", "hard"], default="easy")
    parser.add_argument("--total_episodes", type=int, default=200)
    parser.add_argument("--seed", type=int, default=42)
    parser.add_argument("--max_steps", type=int, default=18)
    parser.add_argument("--num_generations", type=int, default=4)
    parser.add_argument("--learning_rate", type=float, default=1e-5)
    parser.add_argument("--max_prompt_length", type=int, default=1024)
    parser.add_argument("--max_completion_length", type=int, default=256)
    parser.add_argument("--output_dir", default="training/grpo-output")
    args = parser.parse_args()

    try:
        from trl import GRPOConfig, GRPOTrainer
    except ImportError as exc:  # pragma: no cover
        raise SystemExit(
            "TRL (Transformer Reinforcement Learning) is required: "
            "pip install -r requirements-train.txt"
        ) from exc

    logger.info("Loading tokenizer + model: %s", args.model_name)
    tokenizer = AutoTokenizer.from_pretrained(args.model_name)
    if tokenizer.pad_token is None:
        tokenizer.pad_token = tokenizer.eos_token
    model = AutoModelForCausalLM.from_pretrained(
        args.model_name,
        torch_dtype=torch.float32,
    )

    logger.info("Building prompt dataset (%d prompts)", args.total_episodes)
    dataset = build_dataset(
        tokenizer=tokenizer,
        n_prompts=args.total_episodes,
        seed=args.seed,
        scenario=args.scenario,
        difficulty=args.difficulty,
    )

    env = CERNCollisionEnvironment(max_steps=args.max_steps)
    ctx = EpisodeContext(
        env=env,
        seed=args.seed,
        scenario=args.scenario,
        difficulty=args.difficulty,
    )
    reward_fn = make_reward_fn(ctx)

    cfg = GRPOConfig(
        output_dir=args.output_dir,
        per_device_train_batch_size=2,
        gradient_accumulation_steps=2,
        num_generations=args.num_generations,
        learning_rate=args.learning_rate,
        max_prompt_length=args.max_prompt_length,
        max_completion_length=args.max_completion_length,
        logging_steps=5,
        save_steps=50,
        seed=args.seed,
        bf16=False,
        fp16=False,
        report_to=[],
    )

    trainer = GRPOTrainer(
        model=model,
        processing_class=tokenizer,
        train_dataset=dataset,
        reward_funcs=[reward_fn],
        args=cfg,
    )
    logger.info("Starting GRPO training")
    trainer.train()
    trainer.save_model(args.output_dir)
    tokenizer.save_pretrained(args.output_dir)
    logger.info("Saved model to %s", args.output_dir)


if __name__ == "__main__":  # pragma: no cover
    main()