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Scaling Laws For Every Hyperparameter Via Cost-Aware HPO - imbue

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Automatically reproduces the Chinchilla scaling law for LLMs from DeepMind, while also discovering scaling laws for every other hyperparameter, using significantly less compute, and being applicable to any deep learning problem (not just language models) Effectively solves OpenAI’s ProcGen benchmark just by properly tuning a very simple baseline model (PPO, as provided in the original ProcGen paper) Tuning simple baselines can lead to order-of-magnitude performance gains for the same amount of compute, particularly as models are scaled up. 1 2 Some studies have even suggested that the choice of hyperparameters can be more impactful than the choice of model. 3 Perhaps the most striking recent demonstration of the importance of hyperparameter tuning is the Chinchilla scaling laws study from DeepMind, which showed that a 70B parameter language model can outperform a 175B model simply by scaling a single hyperparameter (the number of training tokens). This discovery was transformative, ena

Scaling Laws For Every Hyperparameter Via Cost-Aware HPO - Imbue Article / Research Scaling Laws For Every Hyperparameter Via Cost-Aware HPO 13 min read Last updated 30 Apr 2026 Abraham J. Fetterman Ellie Kitanidis Josh Albrecht CTO, Co-founder Zack Polizzi Engineering Bryden Fogelman Engineering Maksis Knutins Bartosz Wróblewski Jamie Simon Research Kanjun Qiu CEO, Co-founder TL;DR Hyperparameter tuning is massively impactful But systematic tuning is seldom done, particularly on large models Why don’t people tune their networks? Large models are prohibitively expensive to evaluate Current met

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