Conference

We introduce ODRL, the first benchmark tailored for evaluating off-dynamics RL methods where one needs to transfer policies across different domains with dynamics mismatch.

We propose Forward KL regularized Preference optimization for aligning Diffusion policies to align the diffusion policy with preferences, learning to align the policy output with human intents in various tasks.

We propose a new radiology report generation method that aligns the pre-trained model with multiple human preferences via preference-guided multi-objective optimization reinforcement learning.

We introduce ExpoComm, a scalable communication protocol that leverages exponential topologies for efficient information dissemination among many agents in large-scale multi-agent reinforcement learning.

We propose a novel framework that generalizes demonstrations to establish critic-regularized grounding and optimization in the long-term planning of LLMs.

We propose count-based online preference optimization for LLM alignment that leverages coin-flip counting to encourage exploration in online RLHF.

We develop a versatile diffusion planner that can leverage large-scale inferior data that contains task-agnostic sub-optimal trajectories, with the ability to fast adapt to specific tasks.