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Shanghai Jiao Tong University
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Allocating rollout budgets based on state informativeness allows LLM agents to achieve superior performance in complex decision-making tasks without increasing computational costs.
Forget hand-tuning: VisPCO automatically finds optimal visual token pruning configurations in VLMs, outperforming predefined strategies across diverse benchmarks.
Forget memorizing surface patterns: RADAR leverages reinforcement learning to teach LLMs genuine relational reasoning, boosting knowledge graph performance by 5-6%.
