URSA: Understanding and Verifying Chain-of-thought Reasoning in
  Multimodal Mathematics
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.
677f62ca407edfda4408dd07
Ruilin Luo
claimed_verified
2025-01-09T10:06:31.575Z
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Ruilin
user
Antimage01
677f62ca407edfda4408dd08
Zhuofan Zheng
admin_assigned
2025-01-09T11:09:45.251Z
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/avatars/d3f348c1428376aea490339e94d4c239.svg
Zheng Zhuofan
user
fun6668
677f62ca407edfda4408dd09
Yifan Wang
677f62ca407edfda4408dd0a
Yiyao Yu
admin_assigned
2025-01-09T20:19:06.927Z
66b1eb17652012ddfb59e41e
/avatars/b1c8ce44e7a7d789a3eb2e29e2ddfebe.svg
Yiyao Yu
user
yiyaoyu
677f62ca407edfda4408dd0b
Xinzhe Ni
677f62ca407edfda4408dd0c
Zicheng Lin
claimed_verified
2025-01-13T09:09:56.183Z
64292eb375bcc24c5e52c011
/avatars/c8cb03ca35ca12d8831be5f4e8547d54.svg
czl
user
Lin1557
677f62ca407edfda4408dd0d
Jin Zeng
677f62ca407edfda4408dd0e
Yujiu Yang
admin_assigned
2025-01-09T20:02:25.883Z
64ca1fe838837b12d5e529b7
/avatars/44a3ad9e59318784ac531993b5f69f6b.svg
Yujiu Yang
user
Thu-redrobot
URSA: Understanding and Verifying Chain-of-thought Reasoning in
  Multimodal Mathematics
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.
A three-module synthesis strategy and data generation approach improve Chain-of-Thought reasoning in multimodal mathematical tasks, boosting model performance and out-of-distribution verification capabilities.
Chain-of-Thought reasoning Large Language Models derivative process supervision multimodal mathematical reasoning CoT distillation trajectory-format rewriting format unification multimodal mathematics fine-tuning dataset state-of-the-art benchmarks process annotation datasets robust supervision verification capabilities out-of-distribution generalization