Advancing Multimodal Reasoning: From Optimized Cold Start to Staged
  Reinforcement Learning
Inspired by the remarkable reasoning capabilities of Deepseek-R1 in complex
textual tasks, many works attempt to incentivize similar capabilities in
Multimodal Large Language Models (MLLMs) by directly applying reinforcement
learning (RL). However, they still struggle to activate complex reasoning. In
this paper, rather than examining multimodal RL in isolation, we delve into
current training pipelines and identify three crucial phenomena: 1) Effective
cold start initialization is critical for enhancing MLLM reasoning.
Intriguingly, we find that initializing with carefully selected text data alone
can lead to performance surpassing many recent multimodal reasoning models,
even before multimodal RL. 2) Standard GRPO applied to multimodal RL suffers
from gradient stagnation, which degrades training stability and performance. 3)
Subsequent text-only RL training, following the multimodal RL phase, further
enhances multimodal reasoning. This staged training approach effectively
balances perceptual grounding and cognitive reasoning development. By
incorporating the above insights and addressing multimodal RL issues, we
introduce ReVisual-R1, achieving a new state-of-the-art among open-source 7B
MLLMs on challenging benchmarks including MathVerse, MathVision, WeMath,
LogicVista, DynaMath, and challenging AIME2024 and AIME2025.
684117e22db29aa7b403af8d
Shuang Chen
684117e22db29aa7b403af8e
Yue Guo
684117e22db29aa7b403af8f
Zhaochen Su
claimed_verified
2025-06-05T07:26:45.759Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/64264095ba51f8a2136946a0/FR33boVpkDXcrvGMBmprF.jpeg
Zhaochen Su
user
Warrieryes
684117e22db29aa7b403af90
Yafu Li
684117e22db29aa7b403af91
Yulun Wu
684117e22db29aa7b403af92
Jiacheng Chen
claimed_verified
2025-06-05T07:48:38.463Z
65352acb7139c5dd8d9a8590
/avatars/e2ff22b596aee45cdfb8f68dc15572f9.svg
JiachengChen
user
JC-Chen
684117e22db29aa7b403af93
Jiayu Chen
684117e22db29aa7b403af94
Weijie Wang
claimed_verified
2025-06-06T07:41:59.293Z
66699aa8a33847217b5a49c7
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/u8Z-6U8U7ARXOpdBDI7Qm.png
Weijie Wang
user
lhmd
684117e22db29aa7b403af95
Xiaoye Qu
684117e22db29aa7b403af96
Yu Cheng
Advancing Multimodal Reasoning: From Optimized Cold Start to Staged
  Reinforcement Learning
Inspired by the remarkable reasoning capabilities of Deepseek-R1 in complex
textual tasks, many works attempt to incentivize similar capabilities in
Multimodal Large Language Models (MLLMs) by directly applying reinforcement
learning (RL). However, they still struggle to activate complex reasoning. In
this paper, rather than examining multimodal RL in isolation, we delve into
current training pipelines and identify three crucial phenomena: 1) Effective
cold start initialization is critical for enhancing MLLM reasoning.
Intriguingly, we find that initializing with carefully selected text data alone
can lead to performance surpassing many recent multimodal reasoning models,
even before multimodal RL. 2) Standard GRPO applied to multimodal RL suffers
from gradient stagnation, which degrades training stability and performance. 3)
Subsequent text-only RL training, following the multimodal RL phase, further
enhances multimodal reasoning. This staged training approach effectively
balances perceptual grounding and cognitive reasoning development. By
incorporating the above insights and addressing multimodal RL issues, we
introduce ReVisual-R1, achieving a new state-of-the-art among open-source 7B
MLLMs on challenging benchmarks including MathVerse, MathVision, WeMath,
LogicVista, DynaMath, and challenging AIME2024 and AIME2025.