12 papers
Rethinking Personalized Generation: Test-Time Alignment via Factorized Ranking Models
Qiyao Ma, Junshan Zhang, Zhe Zhao
Aligning large language models (LLMs) to diverse user preferences is fundamentally hindered by standard alignment paradigms that optimize for monolithic users. In this work, empiri…
Adaptive Latent Agentic Reasoning
Dongwon Jung, Peng Shi, Yi Zhang +2
Large reasoning models improve performance by generating extended chain-of-thought (CoT) reasoning, but this behavior becomes inefficient when applied to LLM agents. Current LLM ag…
Personalized RewardBench: Evaluating Reward Models with Human Aligned Personalization
Qiyao Ma, Dechen Gao, Rui Cai +4
Pluralistic alignment has emerged as a critical frontier in the development of Large Language Models (LLMs), with reward models (RMs) serving as a central mechanism for capturing d…
GenAI-based Multi-Agent Reinforcement Learning towards Distributed Agent Intelligence: A Generative-RL Agent Perspective
Hang Wang, Junshan Zhang
Multi-agent reinforcement learning faces fundamental challenges that conventional approaches have failed to overcome: exponentially growing joint action spaces, non-stationary envi…
VITA: Vision-to-Action Flow Matching Policy
Dechen Gao, Boqi Zhao, Andrew Lee +6
Conventional flow matching and diffusion-based policies sample via iterative denoising from standard noise distributions (e.g., Gaussian), and require conditioning modules to repea…
Ego-centric Learning of Communicative World Models for Autonomous Driving
Hang Wang, Dechen Gao, Junshan Zhang
We study multi-agent reinforcement learning (MARL) for tasks in complex high-dimensional environments, such as autonomous driving. MARL is known to suffer from the \textit{partial…
IN-RIL: Interleaved Reinforcement and Imitation Learning for Policy Fine-Tuning
Dechen Gao, Hang Wang, Hanchu Zhou +5
Imitation learning (IL) and reinforcement learning (RL) each offer distinct advantages for robotics policy learning: IL provides stable learning from demonstrations, and RL promote…
Heterogeneous Decision Making in Mixed Traffic: Uncertainty-aware Planning and Bounded Rationality
Hang Wang, Qiaoyi Fang, Junshan Zhang
The past few years have witnessed a rapid growth of the deployment of automated vehicles (AVs). Clearly, AVs and human-driven vehicles (HVs) will co-exist for many years, and AVs w…
AdaWM: Adaptive World Model based Planning for Autonomous Driving
Hang Wang, Xin Ye, Feng Tao +5
World model based reinforcement learning (RL) has emerged as a promising approach for autonomous driving, which learns a latent dynamics model and uses it to train a planning polic…
Towards Unraveling and Improving Generalization in World Models
Qiaoyi Fang, Weiyu Du, Hang Wang +1
World models have recently emerged as a promising approach to reinforcement learning (RL), achieving state-of-the-art performance across a wide range of visual control tasks. This…
EI-Drive: A Platform for Cooperative Perception with Realistic Communication Models
Hanchu Zhou, Edward Xie, Wei Shao +3
The growing interest in autonomous driving calls for realistic simulation platforms capable of accurately simulating cooperative perception process in realistic traffic scenarios.…
CarDreamer: Open-Source Learning Platform for World Model based Autonomous Driving
Dechen Gao, Shuangyu Cai, Hanchu Zhou +3
To safely navigate intricate real-world scenarios, autonomous vehicles must be able to adapt to diverse road conditions and anticipate future events. World model (WM) based reinfor…