1 citations · 1 across the 5 of their papers we have counts for
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See, Foresee, Act: Intent-Aware World Modeling for Proactive Retail Assistance
Honghui Zhang, Anna Min, Chenmeinian Guo +9
World models allow agents to anticipate action consequences, but proactive assistance also requires interpreting incomplete observations and deciding whether to intervene before a…
DynaWeb: Model-Based Reinforcement Learning of Web Agents
Hang Ding, Peidong Liu, Junqiao Wang +7
The development of autonomous web agents, powered by Large Language Models (LLMs) and reinforcement learning (RL), represents a significant step towards general-purpose AI assistan…
Mechanistic Understanding and Mitigation of Language Model Non-Factual Hallucinations
Lei Yu, Meng Cao, Jackie Chi Kit Cheung +1
State-of-the-art language models (LMs) sometimes generate non-factual hallucinations that misalign with world knowledge. To explore the mechanistic causes of these hallucinations,…
Beyond Sparse Rewards: Enhancing Reinforcement Learning with Language Model Critique in Text Generation
Meng Cao, Lei Shu, Lei Yu +4
Reinforcement learning (RL) can align language models with non-differentiable reward signals, such as human preferences. However, a major challenge arises from the sparsity of thes…