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  Tokenformer: Rethinking Transformer Scaling with Tokenized Model Parameters
  Motivation It’s hard to imagine AI today without Transformers. These models are the backbone architecture behind large language models that have revolutionized AI. Their influence, however, is not limited to natural language processing. Transformers are also crucial in other domains, such as computer vision, where Vision Transformers (ViT) play a significant role. As we advance, models grow larger, and training the models from scratch becomes increasingly costly and unsustainable, raising environmental concerns. Introducing Tokenformer The paper we review today is titled: “Tokenformer: Rethinking Transformer Scaling with Tokenized Model Parameters,” and it introduces a fascinating change to the Transformer architecture, named… Read more: Tokenformer: Rethinking Transformer Scaling with Tokenized Model Parameters
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  Generative Reward Models: Merging the Power of RLHF and RLAIF for Smarter AI
  Introduction In recent years, we’ve witnessed tremendous progress in AI, primarily due to the rise of large language models (LLMs) such as LLaMA-3.1. To further enhance LLM capabilities, extensive research focuses on improving their training processes. In this post, we review a recent research paper by Stanford University and SynthLabs that suggests a potential improvement, which may significantly advance AI. The paper is titled “Generative Reward Models”, authored by some of the same individuals behind the widely-used DPO method. LLM Training Process Before diving to Generative Reward Models (GenRMs), let’s do a quick recap for how large language models are trained. Pre-training Stage LLMs are first pre-trained on huge amount of text, to learn general purpose knowledge. This step helps the LLM to be good at predicting the next token in a sequence, so for example given an input such as “write a bedtime _”, the LLM would be able to complete it with a reasonable word, such as “story”. However, after the pre-training stage the model is still not good at following human instructions. For this… Read more: Generative Reward Models: Merging the Power of RLHF and RLAIF for Smarter AI
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  Sapiens: Foundation for Human Vision Models
  Introduction In this post, we dive into a new release by Meta AI, presented in a research paper titled Sapiens: Foundation for Human Vision Models, which presents a family of models that target four fundamental human-centric tasks, which we see in the demo above. Fundamental Human-centric Tasks In the above figure from the paper, we can learn about the tasks targeted by Sapiens. Impressively, Meta AI achieves significant improvement comparing to prior state-of-the-art results for all of these tasks, and in the rest of the post we explain how these models were created. Humans-300M: Curating a Human Images Dataset The… Read more: Sapiens: Foundation for Human Vision Models
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  Mixture of Nested Experts: Adaptive Processing of Visual Tokens
  Motivation In recent years, we use AI for more and more use cases, interacting with models that provide us with remarkable outputs. As we move forward, the models we use are getting larger and larger, and so, an important research domain is to improve the efficiency of using and training AI models. Standard MoE Is Enough? A method which we already touched in a previous post, that became popular for large language models (LLMs), but later on also for computer vision, is Mixture-of-Experts (MoE), which helps to increase models size, without a proportional increase in computational cost. However, it comes… Read more: Mixture of Nested Experts: Adaptive Processing of Visual Tokens

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