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Articles

Grandmaster-Level Chess Without Search

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By Anian Ruoss, Grégoire Delétang, Sourabh Medapati, Jordi Grau-Moya
| huggingface.co
Papers Published on Feb 7 · Featured in Daily Papers on Feb 8 Abstract The recent breakthrough successes in machine learning are mainly attributed to scale: namely large-scale attention-based architectures and datasets of unprecedented scale. This paper investigates the impact of training at scale for chess.
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Language Modeling Is Compression

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By Anian Ruoss, Paul-Ambroise Duquenne, Elliot Catt, Tim Genewein
| huggingface.co
Papers Published on Sep 19 · Featured in Daily Papers on Sep 20 Abstract It has long been established that predictive models can be transformed into lossless compressors and vice versa. Incidentally, in recent years, the machine learning community has focused on training increasingly large and powerful self-supervised (language) models. Since these large language models exhibit impressive predictive capabilities, they are well-positioned to be strong compressors.
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