As such, in games where the marginalization of CCE coincides with the game's Nash equilibria (like

We propose a novel preference modeling approach that utilizes AI feedback and only requires a small amount of human data.

In particular, GenRec achieves competitive recognition performance, offering 75.8% and 87.2% accuracy on SSV2andK400,respectively.

In this section, we expand on our observation that similarity between training samples is not binary. Consider the images shown in Figure 6. As a consequence, any similarity between the anchor image and the so-called'negative' examples is completely ignored. Further, all'positive' examples are considered to be The batch size is set to 16000. We train on 4 A100 GPUs.

The learnt models in the latter domain can then be used for a diverse set of tasks in a zero-shot way, similar to "Contrastive Language-Image

Reconstructing past events requires reasoning across long time horizons.

Motion planning is still an open problem for many disciplines, e.g., robotics, autonomous driving, due to their need for high computational resources that hinder

Experiments of pretraining 410M and 1B models on the C4 dataset demonstrate that MA TES significantly outperforms random data selection on extensive downstream tasks. It doubles the gains achieved by the state-of-the-art data selection approach that leverages larger reference models and reduces the total FLOPs required to reach certain performances by half. Further analyses validate the effectiveness of the locally probed oracle data influence and the approximation with data influence models. Our code is open-sourced at https://github.com/cxcscmu/MA