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 for

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ethics.ai (16 June 2026), “Closing the Feedback Loop: From Experience Extraction to Insight Governance in Verbal Reinforcement Learning,” evidence record 909, https://ethics.ai/record/909 (originally published by arXiv).

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