TIGER-Lab/General-Reasoner-Qwen2.5-14B

General-Reasoner: Advancing LLM Reasoning Across All Domains

<p align="center"> <a href="https://github.com/TIGER-AI-Lab/General-Reasoner" target="blank">💻 Code</a> | <a href="https://arxiv.org/abs/2505.14652" target="blank">📄 Paper</a> | <a href="https://huggingface.co/datasets/TIGER-Lab/WebInstruct-verified" target="blank">📊 Dataset</a> | <a href="https://huggingface.co/collections/TIGER-Lab/general-reasoner-67fe9386e43e046489eac013" target="blank">🤗 Model</a> | <a href="https://tiger-ai-lab.github.io/General-Reasoner/" target="_blank">🌐 Project Page</a> </p>

Overview

<p align="center"> <img src="https://tiger-ai-lab.github.io/General-Reasoner/static/images/teaser.png" alt="General-Reasoner Teaser" width="650"/> </p> <p align="center" style="font-style: italic; font-size: 0.95rem;"> <em> Figure: Effectiveness of <strong>General-Reasoner</strong> trained with diverse verifiable reasoning questions using model-based verifier compared to baseline methods on various reasoning tasks. </em> </p>

General-Reasoner is a training paradigm for large language models (LLMs), designed to robustly enhance reasoning abilities across diverse domains—not just mathematics and coding, but also physics, chemistry, finance, humanities, and more.

Key features:

• —Zero RL Training: Direct reinforcement learning from base LLMs, bypassing intermediate supervised stages.
• —Diverse Reasoning Data: 230K+ high-quality, verifiable questions sourced from the web and filtered for answer verifiability across disciplines.
• —Model-Based Verifier: Compact 1.5B generative verifier model for context-aware, chain-of-thought answer validation, outperforming traditional rule-based methods.

This specific model is the General-Reasoner variant trained based on [Qwen2.5-14B-Base](https://huggingface.co/Qwen/Qwen2.5-14B).

Main Results

General-Reasoner outperforms base and supervised models on a variety of reasoning benchmarks, demonstrating robust generalization across domains:

<p align="center"> <a href="https://github.com/TIGER-AI-Lab/General-Reasoner/raw/refs/heads/gh-pages/static/images/resultsgeneral.png" target="blank"> <img src="https://github.com/TIGER-AI-Lab/General-Reasoner/raw/refs/heads/gh-pages/static/images/results_general.png" alt="Main Results" width="600"> </a> </p>

Citation

If you feel our work is helpful, please cite:

@article{general-reasoner,
  title={{G}eneral-{R}easoner: Advancing LLM Reasoning Across All Domains},
  author={Xueguang Ma and Qian Liu and Dongfu Jiang and Ge Zhang and Zejun Ma and Wenhu Chen},
  year={2025},
  journal={arXiv:2505.14652},
  url={https://arxiv.org/abs/2505.14652}
}