LHE

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.