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[2602.17497] Retrospective In-Context Learning for Temporal Credit Assignment with Large Language Models
Summary
The paper presents a novel approach called Retrospective In-Context Learning (RICL) for enhancing temporal credit assignment in reinforcement learning using large language models (LLMs). It demonstrates improved sample efficiency and generalization in training self-evolving ag...
Why It Matters
This research addresses the critical challenge of sparse feedback in reinforcement learning, proposing a method that leverages LLMs to improve learning efficiency. By enhancing temporal credit assignment, it opens pathways for more effective training of AI agents, which is vital for advancing AI capabilities in various applications.
Key Takeaways
- Introduces Retrospective In-Context Learning (RICL) for better credit assignment.
- Demonstrates significant improvements in sample efficiency over traditional methods.
- Proposes an online learning framework, RICOL, for iterative policy refinement.
- Empirical results show RICL's effectiveness in identifying critical states.
- Highlights potential applications of LLMs in reinforcement learning paradigms.
Computer Science > Machine Learning arXiv:2602.17497 (cs) [Submitted on 19 Feb 2026] Title:Retrospective In-Context Learning for Temporal Credit Assignment with Large Language Models Authors:Wen-Tse Chen, Jiayu Chen, Fahim Tajwar, Hao Zhu, Xintong Duan, Ruslan Salakhutdinov, Jeff Schneider View a PDF of the paper titled Retrospective In-Context Learning for Temporal Credit Assignment with Large Language Models, by Wen-Tse Chen and 6 other authors View PDF HTML (experimental) Abstract:Learning from self-sampled data and sparse environmental feedback remains a fundamental challenge in training self-evolving agents. Temporal credit assignment mitigates this issue by transforming sparse feedback into dense supervision signals. However, previous approaches typically depend on learning task-specific value functions for credit assignment, which suffer from poor sample efficiency and limited generalization. In this work, we propose to leverage pretrained knowledge from large language models (LLMs) to transform sparse rewards into dense training signals (i.e., the advantage function) through retrospective in-context learning (RICL). We further propose an online learning framework, RICOL, which iteratively refines the policy based on the credit assignment results from RICL. We empirically demonstrate that RICL can accurately estimate the advantage function with limited samples and effectively identify critical states in the environment for temporal credit assignment. Extended evalua...
