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33bac25 | 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 | #!/usr/bin/env python3
"""
Reference GRPO training script for agentic coding RL.
Uses execution-verified pass_rate as the reward signal.
Usage:
python train_grpo.py \
--model ./nexus-coder-sft \
--output_dir ./nexus-coder-rl
"""
import argparse
import json
import subprocess
import tempfile
from datasets import load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer
from trl import GRPOTrainer, GRPOConfig
# ---------------------------------------------------------------------------
# Execution reward function (simplified — adapt to your sandbox)
# ---------------------------------------------------------------------------
def execution_reward_fn(completions: list, **kwargs) -> list:
"""
Reward function for GRPO.
Expects completions that contain bash commands or patches.
In a real setup, replay commands in a Docker sandbox and return pass_rate.
"""
rewards = []
for completion in completions:
try:
# Look for ```bash ... ``` blocks
if "```bash" in completion:
cmd = completion.split("```bash")[-1].split("```")[0].strip()
result = subprocess.run(cmd, shell=True, capture_output=True, timeout=30, cwd=tempfile.gettempdir())
reward = 1.0 if result.returncode == 0 else 0.0
else:
reward = 0.0
except Exception:
reward = 0.0
rewards.append(reward)
return rewards
# ---------------------------------------------------------------------------
# Dataset prep
# ---------------------------------------------------------------------------
def load_rl_dataset():
"""Load Nemotron RL SWE pivot dataset and normalize prompts."""
ds = load_dataset("nvidia/Nemotron-RL-Agentic-SWE-Pivot-v1", split="train")
def normalize(example):
params = example.get("responses_create_params", {})
inp = params.get("input", [])
if len(inp) > 0 and isinstance(inp[0], dict):
system = inp[0].get("content", "")
ref = example.get("ref_message", {})
reasoning = ref.get("reasoning_content", "") if isinstance(ref, dict) else ""
return {
"prompt": system,
"completion": reasoning,
}
return {"prompt": "", "completion": ""}
ds = ds.map(normalize, remove_columns=ds.column_names)
ds = ds.filter(lambda x: len(x["prompt"]) > 50)
return ds
# ---------------------------------------------------------------------------
# Main
# ---------------------------------------------------------------------------
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--model", required=True, help="Path to SFT checkpoint")
parser.add_argument("--output_dir", default="./nexus-coder-rl")
parser.add_argument("--epochs", type=int, default=1)
parser.add_argument("--batch_size", type=int, default=1)
parser.add_argument("--grad_accum", type=int, default=16)
parser.add_argument("--lr", type=float, default=1e-6)
parser.add_argument("--max_prompt_length", type=int, default=4096)
parser.add_argument("--max_completion_length", type=int, default=12288)
parser.add_argument("--num_generations", type=int, default=8)
parser.add_argument("--hub_model_id", default=None)
args = parser.parse_args()
print("[1/4] Loading SFT model and tokenizer...")
model = AutoModelForCausalLM.from_pretrained(
args.model,
torch_dtype="bfloat16",
device_map="auto",
trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(args.model, trust_remote_code=True)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
print("[2/4] Loading RL dataset...")
dataset = load_rl_dataset()
print(f" RL dataset size: {len(dataset)} examples")
print("[3/4] Configuring GRPO trainer...")
grpo_config = GRPOConfig(
output_dir=args.output_dir,
num_train_epochs=args.epochs,
per_device_train_batch_size=args.batch_size,
gradient_accumulation_steps=args.grad_accum,
learning_rate=args.lr,
max_prompt_length=args.max_prompt_length,
max_completion_length=args.max_completion_length,
num_generations=args.num_generations,
temperature=0.7,
logging_strategy="steps",
logging_steps=5,
logging_first_step=True,
bf16=True,
gradient_checkpointing=True,
disable_tqdm=True,
push_to_hub=args.hub_model_id is not None,
hub_model_id=args.hub_model_id,
)
trainer = GRPOTrainer(
model=model,
reward_funcs=[execution_reward_fn],
args=grpo_config,
train_dataset=dataset,
processing_class=tokenizer,
)
print("[4/4] Starting GRPO training...")
trainer.train()
trainer.save_model(args.output_dir)
print(f"Done. Model saved to {args.output_dir}")
if __name__ == "__main__":
main()
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