🧠 ReasonFlux Series

Advanced Open-Source LLM Post-Training Suite

Princeton University & PKU & UIUC & University of Chicago & ByteDance Seed

🎯 Mission: Building next-generation reasoning capabilities through innovative LLM post-training algorithms focusing on data selection, reinforcement learning, and inference scaling.

Contents of Repository

• Updates
• Model Family Guide
  • ReasonFlux-PRM (NeurIPS 2025)
  • ReasonFlux-Coder (NeurIPS 2025 Spotlight)
  • ReasonFlux
  • Preliminary Work on Thought Template (NeurIPS 2024 Spotlight)
• Performance Hightlights
• Citation

🚀 What Makes ReasonFlux Series Special?

1. Trajectory-Aware Process Reward Models for Long-CoT Reasoning (ReasonFlux-PRM, NeurIPS 2025)

Trajectory-aware reward models that provide dense supervision for both offline data selection and online policy optimization in long-CoT reasoning.

2. Co-Evolved RL for LLM Coder and Unit Tester (ReasonFlux-Coder, NeurIPS 2025 Spotlight)

Innovative approach where coders and unit testers evolve together through reinforcement learning, creating more robust coding capabilities.

3. Long-CoT Reasoning with Thought Templates (ReasonFlux-Zero/F1)

Revolutionary hierarchical reasoning framework that uses thought templates to guide complex problem-solving, achieving SOTA performance with higher efficiency.

Preliminary Work on Thought Template

Our ReasonFlux-Zero/F1 models are built upon insights from our preliminary work on thought templates—specifically, Buffer of Thoughts (NeurIPS 2024 Spotlight) and SuperCorrect (ICLR 2025). These works introduce high-level, efficient intermediate reasoning patterns that guide and structure the thinking process of large language models.

Updates

• [2025/6/23] 🎉 We introduce ReasonFlux-PRM, a family of trajectory-aware process reward models (PRMs) for long CoT reasoning in LLMs. ReasonFlux-PRM is able to support both offline and online reward supervision, by selecting high-quality training data for model distillation, providing dense process-level rewards for policy optimization during reinforcement learning, and enabling reward-guided test-time scaling. Our trained PRMs including ReasonFlux-PRM-7B and ReasonFlux-PRM-1.5B are now available on HuggingFace-GenX. We also release a 7B advanced thinking and reasoning model ReasonFlux-PRM-Qwen-2.5-7B supervised via our PRM.
• [2025/6/04] 🎉 We release our Co-Evolving RL optimized coding LLMs, ReasonFlux-Coder-7B and ReasonFlux-Coder-14B, which outperform similarly sized Qwen Coders and DeepSeek Coders, and naturally fit into common test-time scaling and agentic coding pipelines. We also release our Long-CoT model ReasonFlux-Coder-4B, outperforming Qwen3-4B while achieving 64.8% efficiency in unit test generation.
• [2025/3/24] 🎉We release ReasonFlux-F1-32B, ReasonFlux-F1-14B, ReasonFlux-F1-7B, a series of SOTA-level reasoning LLMs by leveraging the template-augmented reasoning trajectories collected from our ReasonFlux-Zero. For the training and evaluation scripts, please refer to reasonflux-f1/README.md for detail.
• [2025/2/11]🎉We propose ReasonFlux-Zero, a hierarchical LLM reasoning framework that significantly enhances complex reasoning capabilities, outperforming SOTA models like o1-preview and DeepSeek-V3 on challenging MATH and AIME benchmarks.

Model Family Guide

🎯 Process Reward Models

💻 Coding Models

🧠 Reasoning Models

Performance Highlights

1. Complex Reasoning

2. Code Generation and Reasoning

3. PRMs for Long-CoT Reasoning

We observe that in the downstream offline data selection + SFT setting, ReasonFlux-PRM-7B surpasses the performance of the high-quality, human-curated s1k dataset. We further visualize the score distributions over 1,000 trajectory-response pairs generated by Deepseek-R1 and Gemini. The clearly separated distributions indicate that ReasonFlux-PRM-7B effectively differentiates the quality of responses from different models, offering a robust and reliable reward signal for high-quality data selection.

Under the online settings, ReasonFlux-PRM-7B also surpasses other PRM and rule-based baselines during the GRPO policy optimization.

Citation

@article{yang2025reasonflux,
  title={ReasonFlux: Hierarchical LLM Reasoning via Scaling Thought Templates},
  author={Yang, Ling and Yu, Zhaochen and Cui, Bin and Wang, Mengdi},
  journal={arXiv preprint arXiv:2502.06772},
  year={2025}
}

@article{wang2025reasonfluxcoder,
  title={Co-Evolving LLM Coder and Unit Tester via Reinforcement Learning},
  author={Wang, Yinjie and Yang, Ling and Tian, Ye and Shen, Ke and Wang, Mengdi},
  journal={arXiv preprint arXiv:2506.03136},
  year={2025}
}

@article{zou2025reasonfluxprm,
  title={ReasonFlux-PRM: Trajectory-Aware PRMs for Long Chain-of-Thought Reasoning in LLMs},
  author={Zou, Jiaru and Yang, Ling and Gu, Jingwen and Qiu, Jiahao and Shen, Ke and He, Jingrui and Wang, Mengdi},
  journal={arXiv preprint arXiv:2506.18896},
  year={2025}
}