6 papers
Provable and Practical In-Context Policy Optimization for Self-Improvement
Tianrun Yu, Yuxiao Yang, Zhaoyang Wang +6
We study test-time scaling, where a model improves its answer through multi-round self-reflection at inference. We introduce In-Context Policy Optimization (ICPO), in which an agen…
Mitigating Object Hallucination in Large Vision-Language Models via Image-Grounded Guidance
Linxi Zhao, Yihe Deng, Weitong Zhang +1
The advancement of Large Vision-Language Models (LVLMs) has increasingly highlighted the critical issue of their tendency to hallucinate non-existing objects in the images. To addr…
Energy-Weighted Flow Matching for Offline Reinforcement Learning
Shiyuan Zhang, Weitong Zhang, Quanquan Gu
This paper investigates energy guidance in generative modeling, where the target distribution is defined as , with…
Achieving Constant Regret in Linear Markov Decision Processes
Weitong Zhang, Zhiyuan Fan, Jiafan He +1
We study the constant regret guarantees in reinforcement learning (RL). Our objective is to design an algorithm that incurs only finite regret over infinite episodes with high prob…
Uncertainty-Aware Reward-Free Exploration with General Function Approximation
Junkai Zhang, Weitong Zhang, Dongruo Zhou +1
Mastering multiple tasks through exploration and learning in an environment poses a significant challenge in reinforcement learning (RL). Unsupervised RL has been introduced to add…
Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves
Yihe Deng, Weitong Zhang, Zixiang Chen +1
Misunderstandings arise not only in interpersonal communication but also between humans and Large Language Models (LLMs). Such discrepancies can make LLMs interpret seemingly unamb…