This repository contains the qwen2.5-math-rlep model, which is a key checkpoint from the RLEP training process based on Qwen2.5-Math-7B, as presented in the paper RLEP: Reinforcement Learning with Experience Replay for LLM Reasoning.

Reinforcement learning (RL) for large language models is an energy-intensive endeavor: training can be unstable, and the policy may gradually drift away from its pretrained weights. RLEP -- Reinforcement Learning with Experience rePlay -- is a two-phase framework that first collects verified trajectories and then replays them during subsequent training. At every update step, the policy is optimized on mini-batches that blend newly generated rollouts with these replayed successes. By replaying high-quality examples, RLEP steers the model away from fruitless exploration, focuses learning on promising reasoning paths, and delivers both faster convergence and stronger final performance.

[Paper] [Code] [Checkpoints] [Dataset]

✨ Key Highlights

Rapid early gains: On AIME-2024 RLEP hits the baseline’s peak accuracy by step 135 (the baseline needs 380). On AIME-2025 it surpasses the baseline’s best score after only 50 steps.
Higher final performance: RLEP ultimately lifts the peak accuracy from 38.2% → 39.9% (AIME-2024), 19.8% → 22.3% (AIME-2025), and 77.0% → 82.2% on AMC-2023 benchmark.

🚀 Quick Start (Inference)

Here’s a simple example of running inference with vLLM. First, install vLLM (version ≥ 0.7.3):

pip3 install vllm

After installation, you can load and run the model in your Python code like this:

python

import os

from transformers import AutoModelForCausalLM, AutoTokenizer
from vllm import LLM, SamplingParams

model_path = 'Kwai-Klear/qwen2.5-math-rlep'
sampling_params = SamplingParams(temperature=1.0, top_p=1.0, max_tokens=1024 * 3, n=1)
llm = LLM(
    model=model_path,
    enforce_eager=False,
    tensor_parallel_size=1,
    seed=0,
)

tokenizer = AutoTokenizer.from_pretrained(model_path)
question = '''Find the sum of all integer bases $b>9$ for which $17_b$ is a divisor of $97_b.$'''

prefix="Solve the following math problem step by step. The last line of your response should be of the form Answer: $Answer (without quotes) where $Answer is the answer to the problem.\n\n"
post_fix = '\n\nRemember to put your answer on its own line after "Answer:".'
question_with_instruct = prefix + question + post_fix  # the model is trained with this instruct. 
messages = [{'content': question_with_instruct, 'role':'user'}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

output =llm.generate([text], sampling_params)[0]
answer = output.outputs[0].text

print(question)
print(answer)

To evaluete the model on benchmarks like AIME-2024, AIME-2025 and AMC-2023 etc. please refer to our repo.

Evaluation Results

We evaluated the converged RLEP model at 320 training steps and the DAPO-nodyn-bs64 baseline at 400 steps.

	AIME-2024	AIME-2025	AMC-2023
DAPO	32.6	18.9	77.5
DAPO-nodyn-bs64	37.4	19.4	77.3
RLEP	38.5	21.3	83.0

Citation

If you find our paper or code helpful, we would appreciate it if you could cite our work:

bibtex

@misc{zhang2025rlepreinforcementlearningexperience,
      title={RLEP: Reinforcement Learning with Experience Replay for LLM Reasoning}, 
      author={Hongzhi Zhang and Jia Fu and Jingyuan Zhang and Kai Fu and Qi Wang and Fuzheng Zhang and Guorui Zhou},
      year={2025},
      eprint={2507.07451},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2507.07451}, 
}

Acknowledgement

We conducted our experiments with the VERL framework and the Qwen2.5-7B-Math model, using the dataset and training scripts provided by DAPO. Many thanks to the open-sourced works and the broader community for making these resources available!