A new training recipe claims 5.17x better data efficiency than standard scaling
inductionheads · x · 2026-07-21
A research summary argues that as compute keeps growing and data becomes scarce, the standard recipe for training large language models may be leaving efficiency on the table. Using a **200M-token budget**, the authors report two key findings: - raising **weight decay to around 30× the usual 0.1** helps more than the standard setup - training an **ensemble of models with the same total parameter count** can beat a single larger model Under Chinchilla-style scaling assumptions, they compare several recipes and estimate that aggressively scaling model size and ensemble count together can match baseline performance with **5.17× less data**. The chart in the post contrasts standard training with regularized and ensembling-based recipes, showing the joint scaling approach as the best data-efficiency strategy.
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