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## 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 domainsโnot 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](https://huggingface.co/Qwen/Qwen2.5-7B).** ## Main Results General-Reasoner outperforms base and supervised models on a variety of reasoning benchmarks, demonstrating robust generalization across domains:
