2 citations · 2 across the 7 of their papers we have counts for
7 papers
SAPO: Single-Rollout Autoregressive Policy Optimization for Agentic Reinforcement Learning
Dayang Liang, Lang Feng, Bo An +1
Agentic reinforcement learning (RL) has emerged as an important post-training approach for enhancing the capabilities of Large Language Models (LLMs). However, existing methods fac…
PlanPO: Group Planning-Aware Policy Optimization for Multi-Turn Agentic LLMs
Dayang Liang, Liyuan He, Xuan Feng +3
Group-relative policy optimization has emerged as a key paradigm for training agentic large language models (LLMs) on multi-turn interactive tasks. However, most existing variants…
InterReal: A Unified Physics-Based Imitation Framework for Learning Human-Object Interaction Skills
Dayang Liang, Yuhang Lin, Xinzhe Liu +3
Interaction is one of the core abilities of humanoid robots. However, most existing frameworks focus on non-interactive whole-body control, which limits their practical applicabili…
Task-Aware Exploration via a Predictive Bisimulation Metric
Dayang Liang, Ruihan Liu, Lipeng Wan +2
Accelerating exploration in visual reinforcement learning under sparse rewards remains challenging due to the substantial task-irrelevant variations. Despite advances in intrinsic…
Intrinsic Dynamics-Driven Generalizable Scene Representations for Vision-Oriented Decision-Making Applications
Dayang Liang, Jinyang Lai, Yunlong Liu
How to improve the ability of scene representation is a key issue in vision-oriented decision-making applications, and current approaches usually learn task-relevant state represen…
Episodic Reinforcement Learning with Expanded State-reward Space
Dayang Liang, Yaru Zhang, Yunlong Liu
Empowered by deep neural networks, deep reinforcement learning (DRL) has demonstrated tremendous empirical successes in various domains, including games, health care, and autonomou…
Sequential Action-Induced Invariant Representation for Reinforcement Learning
Dayang Liang, Qihang Chen, Yunlong Liu
How to accurately learn task-relevant state representations from high-dimensional observations with visual distractions is a realistic and challenging problem in visual reinforceme…