# General-Reasoner: Advancing LLM Reasoning Across All Domains

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

## Overview

General-Reasoner Teaser

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 [Qwen2.5-14B-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:

## Citation If you feel our work is helpful, please cite: ```bibtex @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} } ```