SRPO: Enhancing Multimodal LLM Reasoning via Reflection-Aware
  Reinforcement Learning
Multimodal large language models (MLLMs) have shown promising capabilities in
reasoning tasks, yet still struggle with complex problems requiring explicit
self-reflection and self-correction, especially compared to their unimodal
text-based counterparts. Existing reflection methods are simplistic and
struggle to generate meaningful and instructive feedback, as the reasoning
ability and knowledge limits of pre-trained models are largely fixed during
initial training. To overcome these challenges, we propose Multimodal
Self-Reflection enhanced reasoning with Group Relative Policy Optimization
(SRPO), a two-stage reflection-aware reinforcement learning (RL) framework
explicitly designed to enhance multimodal LLM reasoning. In the first stage, we
construct a high-quality, reflection-focused dataset under the guidance of an
advanced MLLM, which generates reflections based on initial responses to help
the policy model learn both reasoning and self-reflection. In the second stage,
we introduce a novel reward mechanism within the GRPO framework that encourages
concise and cognitively meaningful reflection while avoiding redundancy.
Extensive experiments across multiple multimodal reasoning benchmarks,
including MathVista, MathVision, MathVerse, and MMMU-Pro, using Qwen-2.5-VL-7B
and Qwen-2.5-VL-32B demonstrate that SRPO significantly outperforms
state-of-the-art models, achieving notable improvements in both reasoning
accuracy and reflection quality.
683ec81753981b08324ce57b
Zhongwei Wan
683ec81753981b08324ce57c
Zhihao Dou
683ec81753981b08324ce57d
Che Liu
admin_assigned
2025-06-03T12:38:05.701Z
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liu
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che111
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Yu Zhang
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Dongfei Cui
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2025-06-03T12:38:16.192Z
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Dongfei Cui
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vencen
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Qinjian Zhao
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2025-06-20T12:20:21.482Z
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Albert Zhao 
user
Albert-CAC
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Hui Shen
claimed_verified
2025-09-15T15:10:15.243Z
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Hui Shen
user
Cloudriver
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Jing Xiong
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Yi Xin
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Yifan Jiang
claimed_verified
2025-06-04T09:02:55.155Z
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Yifan Jiang
user
YifanJ
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Yangfan He
683ec81753981b08324ce586
Mi Zhang
683ec81753981b08324ce587
Shen Yan
SRPO: Enhancing Multimodal LLM Reasoning via Reflection-Aware
  Reinforcement Learning
Multimodal large language models (MLLMs) have shown promising capabilities in
reasoning tasks, yet still struggle with complex problems requiring explicit
self-reflection and self-correction, especially compared to their unimodal
text-based counterparts. Existing reflection methods are simplistic and
struggle to generate meaningful and instructive feedback, as the reasoning
ability and knowledge limits of pre-trained models are largely fixed during
initial training. To overcome these challenges, we propose Multimodal
Self-Reflection enhanced reasoning with Group Relative Policy Optimization
(SRPO), a two-stage reflection-aware reinforcement learning (RL) framework
explicitly designed to enhance multimodal LLM reasoning. In the first stage, we
construct a high-quality, reflection-focused dataset under the guidance of an
advanced MLLM, which generates reflections based on initial responses to help
the policy model learn both reasoning and self-reflection. In the second stage,
we introduce a novel reward mechanism within the GRPO framework that encourages
concise and cognitively meaningful reflection while avoiding redundancy.
Extensive experiments across multiple multimodal reasoning benchmarks,
including MathVista, MathVision, MathVerse, and MMMU-Pro, using Qwen-2.5-VL-7B
and Qwen-2.5-VL-32B demonstrate that SRPO significantly outperforms
state-of-the-art models, achieving notable improvements in both reasoning
accuracy and reflection quality.
A two-stage reinforcement learning framework enhances reasoning and self-reflection in multimodal large language models through reflection-focused datasets and a novel reward mechanism.
multimodal large language models reasoning tasks reflection methods reinforcement learning reflection-aware reinforcement learning GRPO framework reward mechanism MathVista MathVision MathVerse MMMU-Pro Qwen-2.5-VL-7B Qwen-2.5-VL-32B reasoning accuracy reflection quality