Closing the Feedback Loop: From Experience Extraction to Insight Governance in Verbal Reinforcement Learning

• URL: http://arxiv.org/abs/2606.17591v1
• Date: Tue, 16 Jun 2026 06:55:55 GMT
• Title: Closing the Feedback Loop: From Experience Extraction to Insight Governance in Verbal Reinforcement Learning
• Abstract summary: Training-free verbal reinforcement learning enables LLM agents to learn from world feedback.<n>We propose a three-layer architecture -- rules, evidence, and skills -- connected by a feedback-driven curation loop.<n>We show that the same accumulated experience either degrades performance below the zero-shot baseline or dramatically improves accuracy and risk-adjusted returns.
• Score: 11.366112232592592
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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.

Related papers

• Group-Reflective Self-Distillation for Agentic Reinforcement Learning [8.041052886037766]
  Group-Reflective Self-Distillation (GRSD) derives capability-aligned and outcome-discriminative guidance from the policy's own verified rollouts.<n>GRSD consistently outperforms competitive baselines and generalizes more effectively to unseen tasks.
  arXiv  Detail & Related papers  (2026-07-30T11:49:46Z)
• HERO: Hindsight-Enhanced Reflection from Environment Observations for Agentic Self-Distillation [50.53459634301361]
  HERO is a hindsight-enhanced self-distillation framework that uses next environment observations as locally aligned feedback.<n> HERO improves task success and reduces unnecessary turns over environment-feedback-only self-distillation and GRPO.
  arXiv  Detail & Related papers  (2026-06-10T01:35:34Z)
• Tree-of-Experience: A Structured Experience-Management Solution for Self-Evolving Agents under Low-Repetition and Implicit-Reward Environments [7.400600301289333]
  We study a more challenging setting: low-repetition tasks with implicit rewards, where past experience is difficult to reuse and feedback is delayed, noisy, and outcome-level.<n>We propose Tree-of-Experience (ToE), a structured experience-management method that organizes, retrieves, validates, and updates agent experience.
  arXiv  Detail & Related papers  (2026-06-05T06:39:16Z)
• Learning with Rare Success but Rich Feedback via Reflection-Enhanced Self-Distillation [71.16738415436458]
  We introduce Reflection-Enhanced Self-Distillation (RESD), a framework that transforms raw failure feedback into an active source of corrective supervision.<n>RESD interprets failed trajectories by generating retrospective reflections to diagnose local errors, and curates a persistent global playbook.<n> Empirical evaluations on multiple continual learning tasks demonstrate that RESD substantially outperforms standard self-distillation baselines.
  arXiv  Detail & Related papers  (2026-05-12T20:46:05Z)
• Internalizing Agency from Reflective Experience [20.650609947690196]
  LEAFE is a framework that internalizes recovery agency from reflective experience.<n>It consistently improves Pass@1 over the base model and achieves higher Pass@k than outcome-driven baselines.
  arXiv  Detail & Related papers  (2026-03-17T17:50:47Z)
• Experiential Reinforcement Learning [22.545003569634982]
  Experiential Reinforcement Learning (ERL) is a training paradigm that embeds an explicit experience-reflection-consolidation loop into the reinforcement learning process.<n>ERL consistently improves learning efficiency and final performance over strong reinforcement learning baselines.<n>These results suggest that integrating explicit self-reflection into policy training provides a practical mechanism for transforming feedback into durable behavioral improvement.
  arXiv  Detail & Related papers  (2026-02-15T01:23:48Z)
• Agent Learning via Early Experience [93.83579011718858]
  A long-term goal of language agents is to learn and improve through their own experience, ultimately outperforming humans in complex, real-world tasks.<n>Most current agents rely on supervised fine-tuning on expert data, which is challenging to scale and generalizes poorly.<n>We study two strategies of using such data: (1) Implicit world modeling, which uses collected states to ground the policy in environment dynamics; and (2) Self-reflection, where the agent learns from its suboptimal actions to improve reasoning and decision-making.
  arXiv  Detail & Related papers  (2025-10-09T17:59:17Z)
• Learning a Dense Reasoning Reward Model from Expert Demonstration via Inverse Reinforcement Learning [50.20267980386502]
  We learn a dense, token-level reward model for process supervision directly from expert demonstrations.<n>The learned reasoning reward serves two complementary roles: (i) it provides step-level feedback to optimise a reasoning policy during training; and (ii) it functions at inference as a critic to rerank sampled traces under fixed compute budgets.
  arXiv  Detail & Related papers  (2025-10-02T09:55:26Z)
• Hindsight-DICE: Stable Credit Assignment for Deep Reinforcement Learning [11.084321518414226]
  We adapt existing importance-sampling ratio estimation techniques for off-policy evaluation to drastically improve the stability and efficiency of so-called hindsight policy methods. Our hindsight distribution correction facilitates stable, efficient learning across a broad range of environments where credit assignment plagues baseline methods.
  arXiv  Detail & Related papers  (2023-07-21T20:54:52Z)
• Learning "What-if" Explanations for Sequential Decision-Making [92.8311073739295]
  Building interpretable parameterizations of real-world decision-making on the basis of demonstrated behavior is essential. We propose learning explanations of expert decisions by modeling their reward function in terms of preferences with respect to "what if" outcomes. We highlight the effectiveness of our batch, counterfactual inverse reinforcement learning approach in recovering accurate and interpretable descriptions of behavior.
  arXiv  Detail & Related papers  (2020-07-02T14:24:17Z)
• Corruption-robust exploration in episodic reinforcement learning [76.19192549843727]
  We study multi-stage episodic reinforcement learning under adversarial corruptions in both the rewards and the transition probabilities of the underlying system. Our framework yields efficient algorithms which attain near-optimal regret in the absence of corruptions. Notably, our work provides the first sublinear regret guarantee which any deviation from purely i.i.d. transitions in the bandit-feedback model for episodic reinforcement learning.
  arXiv  Detail & Related papers  (2019-11-20T03:49:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.