Closing the Feedback Loop: From Experience Extraction to Insight Governance in Verbal Reinforcement Learning
Training-free verbal reinforcement learning enables LLM agents to learn from world feedback -- objective signals such as dynamic task outcomes, market returns, or demand forecasts -- by extracting verbal rules from experience and injecting them as context, updating the agent's behavior without parameter changes. However, in non-stationary environments these agents face a retention-forgetting dilemma: retaining stale insights causes negative transfer, while discarding them causes catastrophic forgetting when conditions recur. We identify four requirements for navigating this dilemma -- outcome-driven evaluation, persistent structured evidence, non-monotonic knowledge lifecycle, and composi...
| Authors | Yanwei Cui, Xing Zhang, Yulong Zhang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 16 June 2026 |
| Last updated at source | 16 June 2026 |
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