4 papers
RAD: Retrieval High-quality Demonstrations to Enhance Decision-making
Lu Guo, Yixiang Shan, Zhengbang Zhu +5
The paper proposes RAD, a method that improves offline reinforcement learning by retrieving high-return states from the dataset and generating sub-trajectories toward these targets…
Capability-Aligned Hierarchical Learning for Tool-Augmented LLMs
Haotong Yang, Ting Long, Yi Chang
Tool learning enables LLMs to invoke external tools to accomplish tasks. Prior studies have demonstrated the effectiveness of a hierarchical structure: a high-level policy handles…
Target-Aligned Bellman Backup for Cross-domain Offline Reinforcement Learning
Wei Liu, Ting Long
Cross-domain offline reinforcement learning (CDRL) aims to improve policy learning in a target domain by leveraging data collected from a source domain. Existing works typically as…
Rethinking Retrieval-Augmented Generation as a Cooperative Decision-Making Problem
Lichang Song, Ting Long, Yi Chang
Retrieval-Augmented Generation (RAG) has demonstrated strong effectiveness in knowledge-intensive tasks by grounding language generation in external evidence. Despite its success,…