Look Again, Think Slowly: Enhancing Visual Reflection in Vision-Language
  Models
Recent advances in text-only "slow-thinking" reasoning have prompted efforts
to transfer this capability to vision-language models (VLMs), for training
visual reasoning models (VRMs). owever, such transfer faces critical
challenges: Effective "slow thinking" in VRMs requires visual
reflection, the ability to check the reasoning process based on visual
information. Through quantitative analysis, we observe that current VRMs
exhibit limited visual reflection, as their attention to visual information
diminishes rapidly with longer generated responses. To address this challenge,
we propose a new VRM Reflection-V, which enhances visual reflection
based on reasoning data construction for cold-start and reward design for
reinforcement learning (RL). Firstly, we construct vision-centered reasoning
data by leveraging an agent that interacts between VLMs and reasoning LLMs,
enabling cold-start learning of visual reflection patterns. Secondly, a visual
attention based reward model is employed during RL to encourage reasoning based
on visual information. Therefore, Reflection-V demonstrates
significant improvements across multiple visual reasoning benchmarks.
Furthermore, Reflection-V maintains a stronger and more consistent
reliance on visual information during visual reasoning, indicating effective
enhancement in visual reflection capabilities.
68c9211f733e345e52ac1f5d
Pu Jian
68c9211f733e345e52ac1f5e
Junhong Wu
68c9211f733e345e52ac1f5f
Wei Sun
68c9211f733e345e52ac1f60
Chen Wang
68c9211f733e345e52ac1f61
Shuo Ren
68c9211f733e345e52ac1f62
Jiajun Zhang
Look Again, Think Slowly: Enhancing Visual Reflection in Vision-Language
  Models
Recent advances in text-only "slow-thinking" reasoning have prompted efforts
to transfer this capability to vision-language models (VLMs), for training
visual reasoning models (VRMs). owever, such transfer faces critical
challenges: Effective "slow thinking" in VRMs requires visual
reflection, the ability to check the reasoning process based on visual
information. Through quantitative analysis, we observe that current VRMs
exhibit limited visual reflection, as their attention to visual information
diminishes rapidly with longer generated responses. To address this challenge,
we propose a new VRM Reflection-V, which enhances visual reflection
based on reasoning data construction for cold-start and reward design for
reinforcement learning (RL). Firstly, we construct vision-centered reasoning
data by leveraging an agent that interacts between VLMs and reasoning LLMs,
enabling cold-start learning of visual reflection patterns. Secondly, a visual
attention based reward model is employed during RL to encourage reasoning based
on visual information. Therefore, Reflection-V demonstrates
significant improvements across multiple visual reasoning benchmarks.
Furthermore, Reflection-V maintains a stronger and more consistent
reliance on visual information during visual reasoning, indicating effective
enhancement in visual reflection capabilities.
Reflection-V enhances visual reasoning by constructing vision-centered data and using a visual attention reward model, improving reliance on visual information.
visual reflection VRMs reasoning data construction cold-start reinforcement learning reward design visual attention visual reasoning benchmarks