2 citations · 4 across the 12 of their papers we have counts for
8 papers · 1 filter
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…
M3HF: Multi-agent Reinforcement Learning from Multi-phase Human Feedback of Mixed Quality
Ziyan Wang, Zhicheng Zhang, Fei Fang +1
Designing effective reward functions in multi-agent reinforcement learning (MARL) is a significant challenge, often leading to suboptimal or misaligned behaviors in complex, coordi…
Distill Not Only Data but Also Rewards: Can Smaller Language Models Surpass Larger Ones?
Yudi Zhang, Lu Wang, Meng Fang +8
Distilling large language models (LLMs) typically involves transferring the teacher model's responses through supervised fine-tuning (SFT). However, this approach neglects the pote…