Large transformer-based foundation models have been commonly used as pre-trained models that can be adapted to different challenging datasets and settings with state-of-the-art generalization performance.

When training deep neural networks, a model's generalization error is often observed to follow a power scaling law dependent both on the model size and the

These Q values are often objectives that offline algorithms aim to approximate.

This paper advances temporal reasoning within dynamically changing high-dimensional noisy observations, focusing on a latent space that characterizes the nonlinear dynamics of objects in their environment.

We draw parallels between the behavior of LLMs and the evolution of human culture, as the latter has been extensively studied by cognitive scientists for decades.

Thus, we propose a novel Bayesian approach to DPS.