title: General-Reasoner: Advancing LLM Reasoning Across All Domains
summary: Reinforcement learning (RL) has recently demonstrated strong potential in
  enhancing the reasoning capabilities of large language models (LLMs).
  Particularly, the "Zero" reinforcement learning introduced by Deepseek-R1-Zero,
  enables direct RL training of base LLMs without relying on an intermediate
  supervised fine-tuning stage. Despite these advancements, current works for LLM
  reasoning mainly focus on mathematical and coding domains, largely due to data
  abundance and the ease of answer verification. This limits the applicability
  and generalization of such models to broader domains, where questions often
  have diverse answer representations, and data is more scarce. In this paper, we
  propose General-Reasoner, a novel training paradigm designed to enhance LLM
  reasoning capabilities across diverse domains. Our key contributions include:
  (1) constructing a large-scale, high-quality dataset of questions with
  verifiable answers curated by web crawling, covering a wide range of
  disciplines; and (2) developing a generative model-based answer verifier, which
  replaces traditional rule-based verification with the capability of
  chain-of-thought and context-awareness. We train a series of models and
  evaluate them on a wide range of datasets covering wide domains like physics,
  chemistry, finance, electronics etc. Our comprehensive evaluation across these
  12 benchmarks (e.g. MMLU-Pro, GPQA, SuperGPQA, TheoremQA, BBEH and MATH AMC)
  demonstrates that General-Reasoner outperforms existing baseline methods,
  achieving robust and generalizable reasoning performance while maintaining
  superior effectiveness in mathematical reasoning tasks.
paper_authors._id: 682d474782567fffe1f99b7a
paper_authors.name: Xueguang Ma
paper_authors.status: claimed_verified
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paper_authors.fullname: Xueguang Ma
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paper_authors.user: MrLight
paper_authors._id: 682d474782567fffe1f99b7b
paper_authors.name: Qian Liu
paper_authors.status: claimed_verified
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paper_authors.fullname: Qian Liu
paper_authors.type: user
paper_authors.user: SivilTaram
paper_authors._id: 682d474782567fffe1f99b7c
paper_authors.name: Dongfu Jiang
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paper_authors.fullname: Dongfu Jiang
paper_authors.type: user
paper_authors.user: DongfuJiang
paper_authors._id: 682d474782567fffe1f99b7d
paper_authors.name: Ge Zhang
paper_authors.status: admin_assigned
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paper_authors.avatarUrl: /avatars/97a57859d7d87a3a8f1bb41d32a72bc2.svg
paper_authors.fullname: Ge Zhang
paper_authors.type: user
paper_authors.user: zhangysk
paper_authors._id: 682d474782567fffe1f99b7e
paper_authors.name: Zejun Ma
paper_authors._id: 682d474782567fffe1f99b7f
paper_authors.name: Wenhu Chen
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-05-21T03:23:52.529Z
paper_authors._id: 6313a86154e6e5d9f0f94e04
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1662232951344-6313a86154e6e5d9f0f94e04.jpeg
paper_authors.fullname: Wenhu Chen
paper_authors.type: user
paper_authors.user: wenhu
paper_title: General-Reasoner: Advancing LLM Reasoning Across All Domains
paper_summary: Reinforcement learning (RL) has recently demonstrated strong potential in
  enhancing the reasoning capabilities of large language models (LLMs).
  Particularly, the "Zero" reinforcement learning introduced by Deepseek-R1-Zero,
  enables direct RL training of base LLMs without relying on an intermediate
  supervised fine-tuning stage. Despite these advancements, current works for LLM
  reasoning mainly focus on mathematical and coding domains, largely due to data
  abundance and the ease of answer verification. This limits the applicability
  and generalization of such models to broader domains, where questions often
  have diverse answer representations, and data is more scarce. In this paper, we
  propose General-Reasoner, a novel training paradigm designed to enhance LLM
  reasoning capabilities across diverse domains. Our key contributions include:
  (1) constructing a large-scale, high-quality dataset of questions with
  verifiable answers curated by web crawling, covering a wide range of
  disciplines; and (2) developing a generative model-based answer verifier, which
  replaces traditional rule-based verification with the capability of
  chain-of-thought and context-awareness. We train a series of models and
  evaluate them on a wide range of datasets covering wide domains like physics,
  chemistry, finance, electronics etc. Our comprehensive evaluation across these
  12 benchmarks (e.g. MMLU-Pro, GPQA, SuperGPQA, TheoremQA, BBEH and MATH AMC)
  demonstrates that General-Reasoner outperforms existing baseline methods,
  achieving robust and generalizable reasoning performance while maintaining
  superior effectiveness in mathematical reasoning tasks.
paper_ai_summary: General-Reasoner enhances large language model reasoning across diverse domains by using a large-scale dataset and generative model-based answer verification, outperforming existing methods.
paper_ai_keywords: reinforcement learning large language models Deepseek-R1-Zero Generative model chain-of-thought context-awareness