Memory Reward Inflation in Self-Improving LLM Agents
Self-improving LLM agents increasingly learn from experience without updating any weights. Each episode is stored in an external memory, scored, and retrieved for similar future tasks to shape later behavior. Viewed through a reward lens, the stored score is a proxy reward for an implicit, non-parametric policy. Each retrieved episode then becomes a policy-improvement step whose reliability hinges on how that score is produced. In deployment, ground-truth labels are unavailable, so the stored reward is at best an LLM assessment. This substitution creates a failure mode, the *Echo Gap*, across the memory-based self-improving agents and model families studied. Incorrect episodes receive inf...
| Authors | Mohammad Asadolahi, Amir Amini, Samira Talebi et al. |
|---|---|
| Primary category | cs.AI |
| Published | 29 June 2026 |
| Last updated at source | 29 June 2026 |
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