title: NEMOTRON-CROSSTHINK: Scaling Self-Learning beyond Math Reasoning
summary: Large Language Models (LLMs) have shown strong reasoning capabilities,
  particularly when enhanced through Reinforcement Learning (RL). While prior
  work has successfully applied RL to mathematical reasoning -- where rules and
  correctness are well-defined -- generalizing these methods to broader reasoning
  domains remains challenging due to limited data, the lack of verifiable reward
  structures, and diverse task requirements. In this work, we propose
  NEMOTRON-CROSSTHINK, a framework that systematically incorporates multi-domain
  corpora, including both synthetic and real-world question-answer pairs, into RL
  training to improve generalization across diverse reasoning tasks.
  NEMOTRON-CROSSTHINK addresses key challenges by (1) incorporating data from
  varied sources spanning STEM, humanities, social sciences, etc.; (2) applying
  structured templates (e.g., multiple-choice and open-ended) to control
  answer-space complexity; (3) filtering for verifiable answers; and (4)
  optimizing data blending strategies that utilizes data from multiple sources
  effectively. Our approach enables scalable and verifiable reward modeling
  beyond mathematics and demonstrates improved accuracies on both math (MATH-500:
  +30.1%, AMC23:+27.5%) and non-math reasoning benchmarks (MMLU-PRO: +12.8%,
  GPQA-DIAMOND: +11.3%, AGIEVAL: +15.1%, SUPERGPQA: +3.8%). Moreover,
  NEMOTRON-CROSSTHINK exhibits significantly improved response efficiency --
  using 28% fewer tokens for correct answers -- highlighting more focused and
  effective reasoning. Through NEMOTRON-CROSSTHINK, we demonstrate that
  integrating multi-domain, multi-format data in RL leads to more accurate,
  efficient, and generalizable LLMs.
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paper_authors.name: Syeda Nahida Akter
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paper_authors.name: Ying Lin
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paper_authors.fullname: Evelina Bakhturina
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paper_authors.name: Eric Nyberg
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paper_authors.fullname: Eric Nyberg
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paper_authors.name: Yejin Choi
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paper_authors.fullname: Yejin Choi
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paper_authors.user: yejinchoinka
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paper_authors.name: Mostofa Patwary
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paper_authors.fullname: Mostofa Patwary
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paper_authors.name: Mohammad Shoeybi
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paper_authors.name: Bryan Catanzaro
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paper_title: NEMOTRON-CROSSTHINK: Scaling Self-Learning beyond Math Reasoning
paper_summary: Large Language Models (LLMs) have shown strong reasoning capabilities,
  particularly when enhanced through Reinforcement Learning (RL). While prior
  work has successfully applied RL to mathematical reasoning -- where rules and
  correctness are well-defined -- generalizing these methods to broader reasoning
  domains remains challenging due to limited data, the lack of verifiable reward
  structures, and diverse task requirements. In this work, we propose
  NEMOTRON-CROSSTHINK, a framework that systematically incorporates multi-domain
  corpora, including both synthetic and real-world question-answer pairs, into RL
  training to improve generalization across diverse reasoning tasks.
  NEMOTRON-CROSSTHINK addresses key challenges by (1) incorporating data from
  varied sources spanning STEM, humanities, social sciences, etc.; (2) applying
  structured templates (e.g., multiple-choice and open-ended) to control
  answer-space complexity; (3) filtering for verifiable answers; and (4)
  optimizing data blending strategies that utilizes data from multiple sources
  effectively. Our approach enables scalable and verifiable reward modeling
  beyond mathematics and demonstrates improved accuracies on both math (MATH-500:
  +30.1%, AMC23:+27.5%) and non-math reasoning benchmarks (MMLU-PRO: +12.8%,
  GPQA-DIAMOND: +11.3%, AGIEVAL: +15.1%, SUPERGPQA: +3.8%). Moreover,
  NEMOTRON-CROSSTHINK exhibits significantly improved response efficiency --
  using 28% fewer tokens for correct answers -- highlighting more focused and
  effective reasoning. Through NEMOTRON-CROSSTHINK, we demonstrate that
  integrating multi-domain, multi-format data in RL leads to more accurate,
  efficient, and generalizable LLMs.
paper_ai_summary: NEMOTRON-CROSSTHINK is a framework that incorporates diverse multi-domain data into RL training to enhance reasoning capabilities and efficiency of LLMs across various tasks and domains.
paper_ai_keywords: Reinforcement Learning LLMs NEMOTRON-CROSSTHINK multi-domain corpora structured templates reward modeling reasoning benchmarks MATH-500 AMC23 MMLU-PRO GPQA-DIAMOND AGIEVAL SUPERGPQA