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LLMs can act as effective action-level supervisors in reinforcement learning, dramatically boosting the sample efficiency of SAC without sacrificing convergence guarantees.
By explicitly modeling motion with a retina-inspired mechanism, MI-DETR achieves a remarkable +26.35 mAP@50 improvement over the best multi-frame baseline for infrared small target detection.
