3 papers
cs.CL2026
See, Infer, Intervene: Proactive World Modeling for Goal-Oriented Social Intelligence
Honghui Zhang, Chenmeinian Guo, Yichen Yu +7
Multimodal retail agents should not only recognize what a customer is doing, but also decide whether and how to assist before an explicit request is made. We study this setting thr…
cs.LG2026
Learning with a Single Rollout via Monte Carlo Pass@k Critic
Fengdi Che, Yang Liu, Lei Yu +4
Estimating token-level advantages in reinforcement learning (RL) for language models remains challenging because scaling up episodic experience collection is expensive. The difficu…
cs.CL2026
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…