AIBullisharXiv – CS AI · Apr 107/10
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Not All Tokens See Equally: Perception-Grounded Policy Optimization for Large Vision-Language Models
Researchers introduce Perception-Grounded Policy Optimization (PGPO), a novel fine-tuning framework that improves how large vision-language models learn from visual inputs by strategically allocating learning signals to vision-dependent tokens rather than treating all tokens equally. Testing on the Qwen2.5-VL series demonstrates an average 18.7% performance boost across multimodal reasoning benchmarks.