Closing the Feedback Loop: From Experience Extraction to Insight Governance in Verbal Reinforcement Learning
arXiv:2606.17591v1 Announce Type: new Abstract: 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 requ
The paper addresses a critical challenge in dynamic AI agent behavior, reflecting the current push towards more adaptable and robust AI systems capable of continuous learning.
This research provides a framework for more stable and efficient learning in AI agents operating in non-stationary environments, which is crucial for their deployment in complex real-world scenarios.
AI agents can now potentially manage the trade-off between retaining past knowledge and adapting to new conditions more effectively, leading to more resilient agent behaviors.
- · AI agents developers
- · Companies deploying AI in dynamic environments
- · Researchers in reinforcement learning
- · AI systems with static knowledge bases
- · Companies reliant on AI that struggles with non-stationary data
Improved performance and reliability of AI agents in real-world applications.
Accelerated development and adoption of autonomous AI agents across various industries.
Enhanced automation and potential for new white-collar workflows to be fully managed by highly adaptable AI.
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Read at arXiv cs.AI