4 papers
One Model for All: Multi-Objective Controllable Language Models
Qiang He, Yucheng Yang, Tianyi Zhou +3
Aligning large language models (LLMs) with human preferences is critical for enhancing LLMs' safety, helpfulness, humor, faithfulness, etc. Current reinforcement learning from huma…
Task Adaptation from Skills: Information Geometry, Disentanglement, and New Objectives for Unsupervised Reinforcement Learning
Yucheng Yang, Tianyi Zhou, Qiang He +3
Unsupervised reinforcement learning (URL) aims to learn general skills for unseen downstream tasks. Mutual Information Skill Learning (MISL) addresses URL by maximizing the mutual…
ATLaS: Agent Tuning via Learning Critical Steps
Zhixun Chen, Ming Li, Yuxuan Huang +3
Large Language Model (LLM) agents have demonstrated remarkable generalization capabilities across multi-domain tasks. Existing agent tuning approaches typically employ supervised f…
Adaptive Regularization of Representation Rank as an Implicit Constraint of Bellman Equation
Qiang He, Tianyi Zhou, Meng Fang +1
Representation rank is an important concept for understanding the role of Neural Networks (NNs) in Deep Reinforcement learning (DRL), which measures the expressive capacity of valu…