{"slug": "closing-the-feedback-loop-from-experience-extraction-to-insight-governance-in", "title": "Closing the Feedback Loop: From Experience Extraction to Insight Governance in Verbal Reinforcement Learning", "summary": "Researchers propose a three-layer architecture for verbal reinforcement learning in LLM agents, addressing the retention-forgetting dilemma in non-stationary environments. The system uses rules, evidence, and skills with a feedback-driven curation loop to improve performance on financial forecasting tasks.", "body_md": "arXiv:2606.17591v1 Announce Type: new\nAbstract: 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 compositional governance -- and show that existing methods invest heavily in experience extraction while underinvesting in insight governance. We propose a three-layer architecture -- rules, evidence, and skills -- connected by a feedback-driven curation loop that closes the governance gap. Rules capture distilled experience from world outcomes; evidence logs track each rule's reliability across episodes; skills govern which rules to apply, how to resolve conflicts, and when to abstain. On financial forecasting as a case study, where world feedback is naturally abundant, noisy, and non-stationary, we show that the same accumulated experience either degrades performance below the zero-shot baseline or dramatically improves accuracy and risk-adjusted returns, depending on whether the curation loop is present.", "url": "https://wpnews.pro/news/closing-the-feedback-loop-from-experience-extraction-to-insight-governance-in", "canonical_source": "https://arxiv.org/abs/2606.17591", "published_at": "2026-06-17 04:00:00+00:00", "updated_at": "2026-06-17 04:24:02.822325+00:00", "lang": "en", "topics": ["large-language-models", "machine-learning", "artificial-intelligence", "ai-agents"], "entities": [], "alternates": {"html": "https://wpnews.pro/news/closing-the-feedback-loop-from-experience-extraction-to-insight-governance-in", "markdown": "https://wpnews.pro/news/closing-the-feedback-loop-from-experience-extraction-to-insight-governance-in.md", "text": "https://wpnews.pro/news/closing-the-feedback-loop-from-experience-extraction-to-insight-governance-in.txt", "jsonld": "https://wpnews.pro/news/closing-the-feedback-loop-from-experience-extraction-to-insight-governance-in.jsonld"}}