Inspired by dataset distillation (DD) [Wang et al., 2018, Zhao et al., (Corollary 1). Extensive experiments on nine datasets of D4RL benchmark [Fu et al., 2020] with multiple environments and data qualities illustrate that our Av-PBC remarkably promotes the OBD performance, Moreover, Av-PBC has a significant convergence speed and requires only a quarter of distillation steps compared to DBC and PBC.

See more details in our project page: https://sites.google.com/view/be-cause.

We demonstrate that plasticity loss is pervasive under domain shift in this regime, and that a number of methods developed to resolve it in other settings fail, sometimes even performing worse than applying no intervention at all. In contrast, we find that a class of "regenerative" methods are able to consistently mitigate plasticity loss in a variety of contexts, including in gridworld tasks and

Reinforcement learning (RL) is gaining significant attention for solving complex Markov decision process (MDP) tasks.

In this paper, we investigate how to achieve stable and smooth skill chaining for long-horizon robotic manipulation tasks.

Large vision-language models (VLMs) fine-tuned on specialized visual instruction-following data have exhibited impressive language reasoning capabilities across various scenarios.

Reinforcement Learning (RL) agents often learn policies that do not generalise across tasks in which the environmental features and optimal skills are different [des Combes et al., 2018, Garcin et al., 2024].