7 citations · 8 across the 14 of their papers we have counts for
14 papers
On the Capability Separation Between World-Model Policy Learning and Imitated World-Action Models
Yang Yu
World-action models predict a future outcome and then infer an associated action. Although this factorization can improve representation learning and data efficiency, it is unclear…
How Should World Models Be Evaluated for Embodied Decision-Making? A Decision-Making-Centric Position
Yang Yu, Shiyuan Zhang, Yifei Sheng +2
World models have become a central abstraction in modern AI. The term now refers to several different objects: action-conditioned environment models, latent imagination models, fut…
MiniOpt: Reasoning to Model and Solve General Optimization Problems with Limited Resources
Ke Zhao, Zixiang Di, Hong Qian +9
Achieving strong optimization generalization across diverse optimization problems while requiring limited training resources remains a challenging problem for optimization-oriented…
Offline Multi-agent Continual Cooperation via Skill Partition and Reuse
Yuchen Xiao, Lei Yuan, Ruiqi Xue +2
Extracting skills from multi-agent offline dataset improves learning efficiency via sharing task-invariant coordination skills among tasks. In settings where tasks occur sequential…
Provably Efficient Policy-Reward Co-Pretraining for Adversarial Imitation Learning
Tian Xu, Zexuan Chen, Zhilong Zhang +4
Adversarial imitation learning (AIL) achieves high-quality imitation compared to behavioral cloning (BC), but demands substantial online environment interaction. Recent empirical w…
Be Your Own Teacher: Steering Protein Language Models via Unsupervised Reward Optimization
Lanqing Li, Shentong Mo, Yang Yu +1
Protein language models (PLMs) have emerged as powerful tools for controllable biomolecular design, yet their post-training adaptation typically relies on costly wet-lab validation…
ReinVBC: A Model-based Reinforcement Learning Approach to Vehicle Braking Controller
Haoxin Lin, Junjie Zhou, Daheng Xu +1
Braking system, the key module to ensure the safety and steer-ability of current vehicles, relies on extensive manual calibration during production. Reducing labor and time consump…
Off-Policy Value-Based Reinforcement Learning for Large Language Models
Peng-Yuan Wang, Ziniu Li, Tian Xu +8
Improving data utilization efficiency is critical for scaling reinforcement learning (RL) for long-horizon tasks where generating trajectories is expensive. However, the dominant R…
VLGOR: Visual-Language Knowledge Guided Offline Reinforcement Learning for Generalizable Agents
Pengsen Liu, Maosen Zeng, Nan Tang +4
Combining Large Language Models (LLMs) with Reinforcement Learning (RL) enables agents to interpret language instructions more effectively for task execution. However, LLMs typical…
Non-Adversarial Imitation Learning Provably Free of Compounding Errors: The Value Flow Mechanism
Tian Xu, Chenyang Wang, Xiaochen Zhai +3
Adversarial imitation learning (AIL) achieves high-quality imitation by mitigating compounding errors inherent to behavioral cloning (BC), yet its adversarial optimization frequent…
Generalist Reward Models: Found Inside Large Language Models
Yi-Chen Li, Tian Xu, Yang Yu +6
The alignment of Large Language Models (LLMs) is critically dependent on reward models trained on costly human preference data. While recent work explores bypassing this cost with…
Policy Optimization in RLHF: The Impact of Out-of-preference Data
Ziniu Li, Tian Xu, Yang Yu
Aligning intelligent agents with human preferences and values is important. This paper examines two popular alignment methods: Direct Preference Optimization (DPO) and Reward-Model…
Reward-Consistent Dynamics Models are Strongly Generalizable for Offline Reinforcement Learning
Fan-Ming Luo, Tian Xu, Xingchen Cao +1
Learning a precise dynamics model can be crucial for offline reinforcement learning, which, unfortunately, has been found to be quite challenging. Dynamics models that are learned…
ReMax: A Simple, Effective, and Efficient Reinforcement Learning Method for Aligning Large Language Models
Ziniu Li, Tian Xu, Yushun Zhang +4
Reinforcement Learning from Human Feedback (RLHF) is key to aligning Large Language Models (LLMs), typically paired with the Proximal Policy Optimization (PPO) algorithm. While PPO…