Elliott/LUFFY-Qwen-Math-7B-Zero

📖Introduction

LUFFY is a reinforcement learning framework that bridges the gap between zero-RL and imitation learning by incorporating off-policy reasoning traces into the training process. Built upon GRPO, LUFFY combines on-policy rollouts with off-policy demonstrations during advantage estimation and introduces policy shaping via regularized importance sampling to emphasize low-probability yet crucial actions.

Key Highlights:

• Off-Policy Guidance: Seamlessly integrates external reasoning traces to bootstrap learning from stronger models.
• Dynamic Balance: Learns when to imitate and when to explore, adapting over the course of training.
• Policy Shaping: Emphasizes important actions often ignored in standard policy gradients, enabling better generalization.

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Inference

Here’s an example of using LUFFY for inference:

from transformers import AutoTokenizer
from vllm import LLM, SamplingParams

model_path="Elliott/LUFFY-Qwen-Math-7B-Zero"

question = "which number is larger? 9.11 or 9.9?"

tokenizer = AutoTokenizer.from_pretrained(model_path)
messages = [{"role": "user", "content": question}]
chat = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

llm = LLM(model=model_path)
params = SamplingParams(temperature=0.6, max_tokens=8192)
outputs = llm.generate([chat], params)
print(outputs[0].outputs[0].text)

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📃Evaluation

LUFFY is evaluated on six competition-level benchmarks, achieving state-of-the-art results among all zero-RL methods. It surpasses both on-policy RL and imitation learning (SFT), especially in generalization:

Model	AIME 2024	AIME 2025	AMC	MATH-500	Minerva	Olympiad	Avg.
Qwen2.5-Math	12.9	4.2	32.6	48.8	10.7	14.8	20.7
Qwen2.5-Math-Instruct	11.4	8.8	48.3	81.2	33.1	38.8	36.9
SimpleRL-Zero	26.3	6.7	55.4	74.4	25.7	35.4	37.3
OpenReasoner-Zero	17.2	15.0	52.3	84.6	33.8	47.1	41.7
PRIME-Zero	17.9	14.7	55.2	79.4	38.2	42.2	41.3
Oat-Zero	31.7	11.0	61.6	79.2	29.8	42.5	42.6
LUFFY	29.5	23.2	66.1	88.4	33.8	56.4	49.6

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LUFFY also generalizes well to out-of-distribution tasks, with over +6.2 average gain on ARC-C, GPQA, and MMLU-Pro.

Model	ARC-c	GPQA-diamond	MMLU-Pro	Avg.
Qwen2.5-Math-7B-Base	18.2	11.1	16.9	15.4
Qwen2.5-Math-7B-Instruct	70.3	24.7	34.1	43.0
SimpleRL-Zero	30.2	23.2	34.5	29.3
OpenReasoner-Zero	66.2	29.8	58.7	51.6
PRIME-Zero	73.3	18.2	32.7	41.4
Oat-Zero	70.1	23.7	41.7	45.2
LUFFY	80.5	39.9	53.0	57.8

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🌻Acknowledgement

LUFFY builds upon veRL and deepscaler, and utilizes vLLM for inference. We utilize Math-Verify for math reasoning evaluation. We thank the open-source community for datasets and backbones, including NuminaMath, OpenR1-Math-220k, Qwen2.5-Math, and DeepSeek-R1 model.

Citation

If you find our model, data, or evaluation code useful, please kindly cite our paper:

@misc{luffy,
      title={Learning to Reason under Off-Policy Guidance}, 
      author={Jianhao Yan and Yafu Li and Zican Hu and Zhi Wang and Ganqu Cui and Xiaoye Qu and Yu Cheng and Yue Zhang},
      year={2025},
      eprint={2504.14945},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2504.14945}, 
}