TIGER-Lab/General-Reasoner-Qwen2.5-7B

General-Reasoner: Advancing LLM Reasoning Across All Domains

💻 Code | 📄 Paper | 📊 Dataset | 🤗 Model | 🌐 Project Page

Overview

Figure: Effectiveness of General-Reasoner trained with diverse verifiable reasoning questions using model-based verifier compared to baseline methods on various reasoning tasks.

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 Qwen2.5-7B-Base.

Main Results

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

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}
}