RoMeRL: Solving the Memory-Reward Trap in Self-Evolving Agents
Yi Yang · hf · 2026-08-11
The research introduces RoMeRL (Reduced-Order Memory Reinforcement Learning) to address feedback dispersion and the memory-reward trap in learning-based memory systems for self-evolving LLM agents.
- Challenges: Trajectory-indexed utilities grow with interaction history, dispersing limited feedback. Additionally, irrelevant experiences may receive misleading utility updates because they are co-retrieved with useful memories.
- Methodology: RoMeRL represents the growing trajectory-indexed utility space using a fixed-dimensional per-task memory state factorized by outcome polarity and memory dynamics. It incorporates new experiences through a fixed set of semantic coordinates, concentrating feedback over a bounded utility support.
- Results: Across ALFWorld and LifelongAgentBench, RoMeRL improves task performance, reduces the Cold-Q ratio by 80.0%, increases feedback density by approx. 6.0 times, decreases maintained memory size by 84.4%, and cuts LLM calls by 21.1%.
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