title: URSA: Understanding and Verifying Chain-of-thought Reasoning in
    Multimodal Mathematics
summary: Chain-of-thought (CoT) reasoning has been widely applied in the mathematical
  reasoning of Large Language Models (LLMs). Recently, the introduction of
  derivative process supervision on CoT trajectories has sparked discussions on
  enhancing scaling capabilities during test time, thereby boosting the potential
  of these models. However, in multimodal mathematical reasoning, the scarcity of
  high-quality CoT training data has hindered existing models from achieving
  high-precision CoT reasoning and has limited the realization of reasoning
  potential during test time. In this work, we propose a three-module synthesis
  strategy that integrates CoT distillation, trajectory-format rewriting, and
  format unification. It results in a high-quality CoT reasoning instruction
  fine-tuning dataset in multimodal mathematics, MMathCoT-1M. We comprehensively
  validate the state-of-the-art (SOTA) performance of the trained URSA-7B model
  on multiple multimodal mathematical benchmarks. For test-time scaling, we
  introduce a data synthesis strategy that automatically generates process
  annotation datasets, known as DualMath-1.1M, focusing on both interpretation
  and logic. By further training URSA-7B on DualMath-1.1M, we transition from CoT
  reasoning capabilities to robust supervision abilities. The trained URSA-RM-7B
  acts as a verifier, effectively enhancing the performance of URSA-7B at test
  time. URSA-RM-7B also demonstrates excellent out-of-distribution (OOD)
  verifying capabilities, showcasing its generalization. Model weights, training
  data and code will be open-sourced.
paper_authors._id: 677f62ca407edfda4408dd07
paper_authors.name: Ruilin Luo
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paper_authors.fullname: Ruilin
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paper_authors.user: Antimage01
paper_authors._id: 677f62ca407edfda4408dd08
paper_authors.name: Zhuofan Zheng
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paper_authors.fullname: Zheng Zhuofan
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paper_authors.user: fun6668
paper_authors._id: 677f62ca407edfda4408dd09
paper_authors.name: Yifan Wang
paper_authors._id: 677f62ca407edfda4408dd0a
paper_authors.name: Yiyao Yu
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paper_authors.fullname: Yiyao Yu
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paper_authors.user: yiyaoyu
paper_authors._id: 677f62ca407edfda4408dd0b
paper_authors.name: Xinzhe Ni
paper_authors._id: 677f62ca407edfda4408dd0c
paper_authors.name: Zicheng Lin
paper_authors.status: claimed_verified
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paper_authors.fullname: czl
paper_authors.type: user
paper_authors.user: Lin1557
paper_authors._id: 677f62ca407edfda4408dd0d
paper_authors.name: Jin Zeng
paper_authors._id: 677f62ca407edfda4408dd0e
paper_authors.name: Yujiu Yang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-09T20:02:25.883Z
paper_authors._id: 64ca1fe838837b12d5e529b7
paper_authors.avatarUrl: /avatars/44a3ad9e59318784ac531993b5f69f6b.svg
paper_authors.fullname: Yujiu Yang
paper_authors.type: user
paper_authors.user: Thu-redrobot
paper_title: URSA: Understanding and Verifying Chain-of-thought Reasoning in
    Multimodal Mathematics
paper_summary: Chain-of-thought (CoT) reasoning has been widely applied in the mathematical
  reasoning of Large Language Models (LLMs). Recently, the introduction of
  derivative process supervision on CoT trajectories has sparked discussions on
  enhancing scaling capabilities during test time, thereby boosting the potential
  of these models. However, in multimodal mathematical reasoning, the scarcity of
  high-quality CoT training data has hindered existing models from achieving
  high-precision CoT reasoning and has limited the realization of reasoning
  potential during test time. In this work, we propose a three-module synthesis
  strategy that integrates CoT distillation, trajectory-format rewriting, and
  format unification. It results in a high-quality CoT reasoning instruction
  fine-tuning dataset in multimodal mathematics, MMathCoT-1M. We comprehensively
  validate the state-of-the-art (SOTA) performance of the trained URSA-7B model
  on multiple multimodal mathematical benchmarks. For test-time scaling, we
  introduce a data synthesis strategy that automatically generates process
  annotation datasets, known as DualMath-1.1M, focusing on both interpretation
  and logic. By further training URSA-7B on DualMath-1.1M, we transition from CoT
  reasoning capabilities to robust supervision abilities. The trained URSA-RM-7B
  acts as a verifier, effectively enhancing the performance of URSA-7B at test
  time. URSA-RM-7B also demonstrates excellent out-of-distribution (OOD)
  verifying capabilities, showcasing its generalization. Model weights, training
  data and code will be open-sourced.
paper_ai_keywords: CoT reasoning Large Language Models (LLMs) derivative process supervision CoT trajectories multimodal mathematical reasoning CoT distillation trajectory-format rewriting format unification MMathCoT-1M state-of-the-art (SOTA) DualMath-1.1M URSA-7B model out-of-distribution (OOD) URSA-RM-7B