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Chinese Academy of Sciences ♣
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UCOB achieves unprecedented performance in agentic reinforcement learning by dynamically refining skill usage through credit-aware self-distillation.
State-of-the-art multimodal models are highly susceptible to kinematic hallucinations, but a simple measurement injection can boost performance by over 10%.
Agentic RL agents can learn faster and perform better by dynamically maintaining a skill bank that combines high-level task guidance with low-level step-by-step decision support.
