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UCOB achieves unprecedented performance in agentic reinforcement learning by dynamically refining skill usage through credit-aware self-distillation.
Adversarial training of large vision models doesn't have to break the bank: CAAT achieves comparable robustness to standard methods by tuning just 6% of the parameters.
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.
