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.