5 papers
Joint Learning of Experiential Rules and Policies for Large Language Model Agents
Shicheng Ye, Chao Yu
For LLM agents in multi-step interactive environments, a key challenge is to make effective use of accumulated interaction experience. Existing work has typically separated two use…
MotionHalluc: Diagnosing Kinematic Hallucinations in Fine-Grained Motion Reasoning
Weile Guo, Shenghong He, Danying Mo +3
Motion instruction generation in cross-video comparison aims to produce corrective feedback that describes the differences between a query and a reference motion. However, existing…
Adaptive Coarse-to-Fine Subgoal Refinement for Long-Horizon Offline Goal-Conditioned Reinforcement Learning
Kaiqiang Ke, Shenghong He, Chengdong Xu +3
Offline goal-conditioned reinforcement learning (GCRL) is challenging in long-horizon tasks, where distant state--goal pairs provide weak supervision and value estimates become vul…
Context-Picker: Dynamic context selection using multi-stage reinforcement learning
Siyuan Zhu, Chengdong Xu, Kaiqiang Ke +1
In long-context question answering, selecting the appropriate scope of context for a query remains a key and unresolved challenge. Insufficient context can lead to missing essentia…
HR: Hierarchical Hindsight Reflection for Multi-Task LLM Agents
Shicheng Ye, Chao Yu, Kaiqiang Ke +2
Large language model (LLM)-based agents have shown strong potential in multi-task scenarios, owing to their ability to transfer knowledge across diverse tasks. However, existing ap…