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 repo contains the Diverse Reasoning Data WebInstruct-verified **

Dataset Details

We construct a diverse, high‑quality dataset to facilitate robust reasoning capabilities across a broad range of domains, extending beyond the commonly studied mathematical problems.

• We trace back the data in WebInstruct to its original web page to re‑crawl the question–answer pairs.
  If the original page lacks human‑written answers, we drop the entry. This ensures every re‑crawled item is human‑verified and, therefore, that each answer is of reliable quality.

• Gemini‑1.5‑Pro is employed to selectively extract questions with clearly verifiable short answers, further boosting dataset reliability.

• Gemini‑2.0‑Flash then generates eight candidate answers per question for additional filtering:

  • We discard any question for which all eight Gemini‑generated answers are incorrect, eliminating ambiguous or noisy items that arose during web scraping.
  • We also remove overly simple questions—those for which all eight candidate answers are correct—to preserve dataset complexity and better challenge model generalization.

These steps ensure the correctness of the constructed dataset.

Distribution

The distribution of disciplines is depicted as follows:

Verification

The short answers have different forms, including float, array, matrix, latex, etc. To verify these answers, please use GPT/Gemini or use the locally-served model at https://huggingface.co/TIGER-Lab/general-verifier.

Notes

• As discussed in discussion_3, noticed by @zlk, some multiple choice questions have options missing from the question. We have fixed this in the latest dataset. The original version of the dataset can be found in the train_legacy split.

Citation

If you feel our work is helpful, please cite:

@inproceedings{
  ma2025generalreasoner,
  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},
  booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems},
  year={2025},
  url={https://openreview.net/forum?id=pBFVoll8Xa}
}