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Reinforcement Learning over Memory

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From Player to Master: Enhancing Test-Time Learning of LLM Agents via Reinforcement Learning over Memory

Announce Type: new Abstract: Large language model (LLM) agents are increasingly deployed in long-running settings where improving through experience at test time becomes important. A common approach is to update an explicit memory after each interaction to guide future decisions. However, most existing methods rely on hand-designed prompting rules, making it difficult to align memory updates with downstream objectives over multi-step horizons consistently.

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Why Linear Recurrent Memory Works in Partially Observable Reinforcement Learning

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Just-In-Time Reinforcement Learning: Continual Learning in LLM Agents Without Gradient Updates

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Episodic Memory Temporal Consistency for Cooperative Multi-Agent Reinforcement Learning

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Dynamic Multi-Pair Trading Strategy in Cryptocurrency Markets with Deep Reinforcement Learning

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Deep reinforcement learning with spatial and temporal awareness for active boundary control of buoyancy-driven convection

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