An open-source framework for GPU-accelerated robot learning and sim-to-real transfer

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Summary While the success of large-scale deep learning models has hinged on the ``bigger is better'' approach – scaling model size and training data – this paradigm may rapidly be reaching an inflec...

The increasing complexity of modern machine learning models exposes the limitations of the traditional, monolithic approach to their development, raising concerns about cost and...

Tutorial on the technical background behind OpenAI o1. Talk written with Daniel Ritter.Slides: https://github.com/srush/awesome-o1Talk: The “large” in LLM is...

Data Parallelism, Butterfly All-Reduce, Gossiping and More…

In this essay, we take a tour of how AI is transforming scientific disciplines from genomics to computer science to weather forecasting. Some scientists are training their own AI models, while...

Large Language Models (LLMs) generate text by sampling the next token from a probability distribution over the vocabulary at each decoding step. However, popular sampling methods like top-p (nucleus…