General-Reasoner: Advancing LLM Reasoning Across All Domains
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.
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Xueguang Ma
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Xueguang Ma
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Qian Liu
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Qian Liu
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SivilTaram
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Dongfu Jiang
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Dongfu Jiang
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Ge Zhang
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2025-05-21T10:15:29.552Z
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Ge Zhang
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zhangysk
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Zejun Ma
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Wenhu Chen
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2025-05-21T03:23:52.529Z
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Wenhu Chen
user
wenhu
General-Reasoner: Advancing LLM Reasoning Across All Domains
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.
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.
reinforcement learning large language models Deepseek-R1-Zero Generative model chain-of-thought context-awareness