4 citations · 10 across the 18 of their papers we have counts for
9 papers · 1 filter
BRiTE: Bootstrapping Reinforced Thinking Process to Enhance Language Model Reasoning
Han Zhong, Yutong Yin, Shenao Zhang +10
Large Language Models (LLMs) have demonstrated remarkable capabilities in complex reasoning tasks, yet generating reliable reasoning processes remains a significant challenge. We p…
Reward-Augmented Data Enhances Direct Preference Alignment of LLMs
Shenao Zhang, Zhihan Liu, Boyi Liu +6
Preference alignment in Large Language Models (LLMs) has significantly improved their ability to adhere to human instructions and intentions. However, existing direct alignment alg…
Self-Exploring Language Models: Active Preference Elicitation for Online Alignment
Shenao Zhang, Donghan Yu, Hiteshi Sharma +6
Preference optimization, particularly through Reinforcement Learning from Human Feedback (RLHF), has achieved significant success in aligning Large Language Models (LLMs) to adhere…
Provably Mitigating Overoptimization in RLHF: Your SFT Loss is Implicitly an Adversarial Regularizer
Zhihan Liu, Miao Lu, Shenao Zhang +5
Aligning generative models with human preference via RLHF typically suffers from overoptimization, where an imperfectly learned reward model can misguide the generative model to ou…
How Can LLM Guide RL? A Value-Based Approach
Shenao Zhang, Sirui Zheng, Shuqi Ke +6
Reinforcement learning (RL) has become the de facto standard practice for sequential decision-making problems by improving future acting policies with feedback. However, RL algorit…
Sample-Efficient Multi-Agent RL: An Optimization Perspective
Nuoya Xiong, Zhihan Liu, Zhaoran Wang +1
We study multi-agent reinforcement learning (MARL) for the general-sum Markov Games (MGs) under the general function approximation. In order to find the minimum assumption for samp…
Maximize to Explore: One Objective Function Fusing Estimation, Planning, and Exploration
Zhihan Liu, Miao Lu, Wei Xiong +6
In online reinforcement learning (online RL), balancing exploration and exploitation is crucial for finding an optimal policy in a sample-efficient way. To achieve this, existing s…
Guarded Policy Optimization with Imperfect Online Demonstrations
Zhenghai Xue, Zhenghao Peng, Quanyi Li +2
The Teacher-Student Framework (TSF) is a reinforcement learning setting where a teacher agent guards the training of a student agent by intervening and providing online demonstrati…
Provably Efficient Generative Adversarial Imitation Learning for Online and Offline Setting with Linear Function Approximation
Zhihan Liu, Yufeng Zhang, Zuyue Fu +2
In generative adversarial imitation learning (GAIL), the agent aims to learn a policy from an expert demonstration so that its performance cannot be discriminated from the expert p…