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arXiv · 2512.01766

On the Unreasonable Effectiveness of Last-layer Retraining

John C. Hill·Tyler LaBonte·Xinchen Zhang·Vidya Muthukumar
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Abstract

Last-layer retraining (LLR) methods -- wherein the last layer of a neural network is reinitialized and retrained on a held-out set following ERM training -- have garnered interest as an efficient approach to rectify dependence on spurious correlations and improve performance on minority groups. Surprisingly, LLR has been found to improve worst-group accuracy even when the held-out set is an imbalanced subset of the training set. We initially hypothesize that this ``unreasonable effectiveness'' of LLR is explained by its ability to mitigate neural collapse through the held-out set, resulting in the implicit bias of gradient descent benefiting robustness. Our empirical investigation does not support this hypothesis. Instead, we present strong evidence for an alternative hypothesis: that the success of LLR is primarily due to better group balance in the held-out set. We conclude by showing how the recent algorithms CB-LLR and AFR perform implicit group-balancing to elicit a robustness improvement.

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John C. Hill, Tyler LaBonte, Xinchen Zhang, Vidya Muthukumar. 2025-12-01. On the Unreasonable Effectiveness of Last-layer Retraining. https://arxiv.org/abs/2512.01766

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