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It is demonstrated that a general-purpose agent can directly drive a physical robot throughout task execution without any task-specific or environment-specific training and Agent as Policy (AGP) is introduced, which places task planning and execution under the agent's control.
Multimodal unlearning could revolutionize how we handle sensitive data in AI, enabling targeted removal without sacrificing model performance.
Rewarding *correct* answers in multimodal reasoning can actually *worsen* reasoning quality, but a simple groupwise ranking of solution trajectories significantly boosts reliability.
