Closing the Feedback Loop: From Experience Extraction to Insight Governance in Verbal Reinforcement Learning
Quick Answer
The paper introduces a three-layer architecture for verbal reinforcement learning that addresses the retention-forgetting dilemma in non-stationary environments.
Quick Take
By implementing a feedback-driven curation loop, the model enhances performance in financial forecasting, demonstrating that accumulated experience can either degrade or improve outcomes significantly based on governance. This approach emphasizes the need for structured evidence and compositional governance in agents.
Key Points
- Proposes a three-layer architecture: rules, evidence, and skills for LLM agents.
- Addresses retention-forgetting dilemma with outcome-driven evaluation and structured evidence.
- Demonstrates improved accuracy and risk-adjusted returns in financial forecasting.
- Existing methods focus on experience extraction but neglect insight governance.
- Curation loop crucial for leveraging accumulated experience effectively.
Paper Resources
Article Content
From source RSS / original summaryarXiv:2606. 17591v1 Announce Type: new Abstract: Training-free verbal reinforcement learning enables 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.
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from arXiv cs.AI
See more →HOBA: Hierarchical On-Policy Bidding Agents for Adaptive Online Advertising
HOBA (Hierarchical On-policy Bidding Agents) is a novel hierarchical reinforcement learning framework that enhances online advertising bidding systems by improving adaptability and reducing hyperparameter tuning costs. It utilizes a for hyperparameter inference, a SARSA agent for expert model selection, and a dynamic expert pool for bid execution, achieving a +3.6% increase in target cost during large-scale deployment and outperforming state-of-the-art baselines on AuctionNet.