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ccbd209 9bd725a ccbd209 9bd725a ccbd209 0e35fb1 ccbd209 | 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 | """The RL core: GRPO with the judge as the reward model.
This is the "constantly improving via reinforcement" engine. For each prompt the policy
samples GRPO_NUM_GENERATIONS candidates; the judge scores each (reward); GRPO nudges the
policy toward the higher-scoring ones (group-relative advantage). No human labels needed
in the loop -- the judge IS the reward -- which is exactly why the eval gate (evalgate.py)
is mandatory to catch reward-hacking / collapse.
"""
from pathlib import Path
def run_grpo(policy_model_name, prompts, types, judge, out_dir, steps, num_generations, lr, lora_r, lora_alpha):
import torch
from datasets import Dataset
from peft import LoraConfig
from trl import GRPOConfig, GRPOTrainer
from core.judge import make_reward_func
# the "type" column rides along and reaches the reward_func as a kwarg, so each
# completion is scored by the right modality verifier + judge.
ds = Dataset.from_dict({"prompt": prompts, "type": types})
cfg = GRPOConfig(
output_dir=out_dir,
learning_rate=lr,
per_device_train_batch_size=num_generations,
num_generations=num_generations,
max_steps=steps,
bf16=True,
gradient_checkpointing=True,
logging_steps=10,
save_strategy="no",
report_to="none",
)
peft_cfg = LoraConfig(r=lora_r, lora_alpha=lora_alpha, task_type="CAUSAL_LM",
target_modules=["q_proj", "k_proj", "v_proj", "o_proj"])
trainer = GRPOTrainer(
model=policy_model_name,
reward_funcs=[make_reward_func(judge)],
args=cfg,
train_dataset=ds,
peft_config=peft_cfg,
)
trainer.train()
# merge the LoRA into the base so the next stage loads a full model (not a bare adapter)
from transformers import AutoTokenizer
merged = trainer.model.merge_and_unload()
merged.save_pretrained(out_dir)
AutoTokenizer.from_pretrained(policy_model_name).save_pretrained(out_dir)
return out_dir
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