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Forget trial-and-error: this paper derives hyperparameter scaling laws for modern optimizers directly from convergence bounds, potentially automating and optimizing the hyperparameter tuning process.
By strategically guiding self-play with challenging real-world examples, GASP unlocks a 2.5% performance boost in coding LLMs and conquers previously unsolvable problems.
Forget scaling up: smarter state mixing in linear recurrent networks lets you match LSTMs and Mamba on sequence tasks, closing the expressivity gap without sacrificing efficiency.
