Modern detection transformers (DETRs) use a set of object queries to predict a list of
bounding boxes, sort them by their classification confidence scores, and select the top …

This paper identifies significant redundancy in the query-key interactions within self-attention
mechanisms of diffusion transformer models, particularly during the early stages of …

Offline-to-online reinforcement learning (RL) is a training paradigm that combines pre-
training on a pre-collected dataset with fine-tuning in an online environment. However, the …

The divergence of the Q-value estimation has been a prominent issue offline reinforcement
learning (offline RL), where the agent has no access to real dynamics. Traditional beliefs …

Offline reinforcement learning (RL), which operates solely on static datasets without further
interactions with the environment, provides an appealing alternative to learning a safe and …

Model-based reinforcement learning (RL), which learns environment model from offline
dataset and generates more out-of-distribution model data, has become an effective …

Offline reinforcement learning (RL) is challenged by the distributional shift problem. To
tackle this issue, existing works mainly focus on designing sophisticated policy constraints …