Transformers, originally devised for natural language processing (NLP), have also produced
significant successes in computer vision (CV). Due to their strong expression power …

Recent advancements in offline reinforcement learning (RL) have underscored the
capabilities of Return-Conditioned Supervised Learning (RCSL), a paradigm that learns the …

Deep generative models (DGMs) have demonstrated great success across various domains,
particularly in generating texts, images, and videos using models trained from offline data …

Diffusion models have been adopted for behavioral cloning in a sequence modeling
fashion, benefiting from their exceptional capabilities in modeling complex data distributions …

In numerous artificial intelligence applications, the collaborative efforts of multiple intelligent
agents are imperative for the successful attainment of target objectives. To enhance …

Prompt-tuning has emerged as a promising method for adapting pre-trained models to
downstream tasks or aligning with human preferences. Prompt learning is widely used in …

Designing better deep networks and better reinforcement learning (RL) algorithms are both
important for deep RL. This work studies the former. Specifically, the Perception and …

Recent advancements in offline reinforcement learning (RL) have underscored the
capabilities of Conditional Sequence Modeling (CSM), a paradigm that learns the action …

The purpose of offline multi-task reinforcement learning (MTRL) is to develop a unified policy
applicable to diverse tasks without the need for online environmental interaction. Recent …

Graph Transformers (GTs) have demonstrated their advantages across a wide range of
tasks. However, the self-attention mechanism in GTs overlooks the graph's inductive biases …