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Chemical VLMs can achieve SOTA performance by prioritizing reasoning through fine-grained analysis of visual inputs, such as identifying functional groups, before generating answers.
Unlock 2x faster reinforcement learning by distilling group feedback into actionable language refinements that guide exploration.
