Before that, it is crucial to develop benchmarks to evaluate L VLMs' effectiveness in various medical applications.

RL benchmark tailored for embodied pixel-based RL.

Multi-distribution or collaborative learning involves learning a single predictor that works well across multiple data distributions, using samples from each during training.

This makes it easily extensible and much more expressive than existing toolkits. It supports all 9 and all 10 of the decision-based group metrics of two popular review articles.

Sharpness-A ware Minimization (SAM) is a recently proposed gradient-based optimizer (Foret et al., ICLR 2021) that greatly improves the prediction performance of deep neural networks.