Visually Grounded Self-Reflection for Vision-Language Models via Reinforcement Learning
Large vision-language models can reason over multimodal inputs by generating textual chains of thought (CoT). A key capability exhibited in CoT reasoning is self-reflection: revisiting earlier decisions and correcting previous errors. However, existing LVLMs often fail to properly attend to visual inputs during reflection, limiting their ability to translate feedback into grounded corrections, especially for out-of-distribution images. To address this issue, we propose a novel reinforcement learning training framework VRRL, with two components explicitly designed to elicit visually grounded se
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- FuzzySimilar title/name (fuzzy) · 59%VioletVision-3B →
“Fuzzy title match (0.73): “Visually Grounded Self-Reflection for Vision-Language Models” ≈ “VioletVision-3B””
- LinkedLinked via arxiv author · 85%Liyan Tang →
“Visually Grounded Self-Reflection for Vision-Language Models via Reinforcement Learning”
- LinkedLinked via arxiv author · 85%Fangcong Yin →
“Visually Grounded Self-Reflection for Vision-Language Models via Reinforcement Learning”
- LinkedLinked via arxiv author · 85%Greg Durrett →
“Visually Grounded Self-Reflection for Vision-Language Models via Reinforcement Learning”
- PossiblePossibly related (embedding) · 48%om-ai-lab/VLM-R1 →
- FuzzySimilar title/name (fuzzy) · 84%pytorch/vision →
“Fuzzy title match (0.92): “Visually Grounded Self-Reflection for Vision-Language Models” ≈ “pytorch/vision””
- FuzzyOverlapping authors or contributors · 62%rasbt/LLMs-from-scratch →
“Shared author/contributor keys: yin”
- FuzzySimilar title/name (fuzzy) · 59%aymericdamien/TopDeepLearning →
“Fuzzy title match (0.73): “Visually Grounded Self-Reflection for Vision-Language Models” ≈ “aymericdamien/TopDeepLearning””