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This paper introduces Novelty and Surprise Prioritized Experience Replay (NSPER) to enhance sample efficiency in image-based reinforcement learning by combining novelty and surprise for experience prioritization and exploration.
Search-G1 proposes a representation-based intrinsic reward framework for search-augmented language agents, using intervention-calibrated readouts to balance retrieval necessity and evidence reliance, improving search efficiency without costly annotations.