8 citations · 9 across the 4 of their papers we have counts for
4 papers
Response-Level Rewards Are All You Need for Online Reinforcement Learning in LLMs: A Mathematical Perspective
Shenghua He, Tian Xia, Xuan Zhou +1
We study a common challenge in reinforcement learning for large language models (LLMs): the Zero-Reward Assumption, where non-terminal actions (i.e., intermediate token generations…
Facilitating Long Context Understanding via Supervised Chain-of-Thought Reasoning
Jingyang Lin, Andy Wong, Tian Xia +4
Recent advances in Large Language Models (LLMs) have enabled them to process increasingly longer sequences, ranging from 2K to 2M tokens and even beyond. However, simply extending…
PlanGenLLMs: A Modern Survey of LLM Planning Capabilities
Hui Wei, Zihao Zhang, Shenghua He +3
LLMs have immense potential for generating plans, transforming an initial world state into a desired goal state. A large body of research has explored the use of LLMs for various p…
Systematic Evaluation of LLM-as-a-Judge in LLM Alignment Tasks: Explainable Metrics and Diverse Prompt Templates
Hui Wei, Shenghua He, Tian Xia +4
LLM-as-a-Judge has been widely applied to evaluate and compare different LLM alignmnet approaches (e.g., RLHF and DPO). However, concerns regarding its reliability have emerged, du…