We present a novel self-supervised approach for hierarchical representation learning and segmentation of perceptual inputs in a streaming fashion.

Large pretrained models can be used as annotators, helping replace or augment crowdworkers and enabling distilling generalist models into smaller specialist models.

Hence, the performance of LLMs in various NLP tasks depends significantly onthecrucial roleplayedbytheattention mechanism with thesoftmaxunit.

The training of language models is typically not an end-to-end process.

Linear RNN architectures, like Mamba, can be competitive with Transformer models in language modeling while having advantageous deployment characteristics. Given the focus on training large-scale Transformer models, we consider the challenge of converting these pretrained models for deployment.

Tree-based models, particularly favored for handling tabular data (Grinsztajn et al., 2022), continue

While previous research has advocated for constraining policy optimization, our study introduces a novel approach to enhance the reward model's