9 papers
Causality Meets Locality: Provably Generalizable and Scalable Policy Learning for Networked Systems
Hao Liang, Shuqing Shi, Yudi Zhang +2
Large-scale networked systems, such as traffic, power, and wireless grids, challenge reinforcement-learning agents with both scale and environment shifts. To address these challeng…
PillagerBench: Benchmarking LLM-Based Agents in Competitive Minecraft Team Environments
Olivier Schipper, Yudi Zhang, Yali Du +2
LLM-based agents have shown promise in various cooperative and strategic reasoning tasks, but their effectiveness in competitive multi-agent environments remains underexplored. To…
Learning Instruction-Following Policies through Open-Ended Instruction Relabeling with Large Language Models
Zhicheng Zhang, Ziyan Wang, Yali Du +1
Developing effective instruction-following policies in reinforcement learning remains challenging due to the reliance on extensive human-labeled instruction datasets and the diffic…
Abstract Counterfactuals for Language Model Agents
Edoardo Pona, Milad Kazemi, Yali Du +2
Counterfactual inference is a powerful tool for analysing and evaluating autonomous agents, but its application to language model (LM) agents remains challenging. Existing work on…
GRU: Mitigating the Trade-off between Unlearning and Retention for LLMs
Yue Wang, Qizhou Wang, Feng Liu +4
Large language model (LLM) unlearning has demonstrated its essential role in removing privacy and copyright-related responses, crucial for their legal and safe applications. Howeve…
ATLaS: Agent Tuning via Learning Critical Steps
Zhixun Chen, Ming Li, Yuxuan Huang +3
Large Language Model (LLM) agents have demonstrated remarkable generalization capabilities across multi-domain tasks. Existing agent tuning approaches typically employ supervised f…