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

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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.

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Authors: Yanwei Cui, Xing Zhang, Yulong Zhang, Li Shao, Xiaofeng Shi, Guanghui Wang, Peiyang He
Released: 2026-06-18 (21d ago)
Topic: Related to current project
Relation to my work: Read next because Closing the Feedback Loop: From Experience Extraction to Insight Governance in Verbal Reinforcement Learning overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: text, under, eval, line, extraction, without, position. Source: arxiv cs.AI (Artificial Intelligence).
arXiv: 2606.17591

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