(How) Learning Rates Regulate Catastrophic Overtraining
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arXiv
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866911595197628416 |
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| author | Rofin, Mark Varre, Aditya Flammarion, Nicolas |
| author_facet | Rofin, Mark Varre, Aditya Flammarion, Nicolas |
| contents | Supervised fine-tuning (SFT) is a common first stage of LLM post-training, teaching the model to follow instructions and shaping its behavior as a helpful assistant. At the same time, SFT may harm the fundamental capabilities of an LLM, particularly after long pretraining: a phenomenon known as catastrophic overtraining (Springer et al., 2025). To understand overtraining, we first investigate catastrophic forgetting in finetuning through the lens of implicit regularization of the learning rate. For models trained to the same SFT loss, we identify how the learning rate mediates optimization: finetuning with large and small steps converges to qualitatively different models. Next, we link forgetting to overtraining: learning rate decay increases the sharpness of the pretrained model, which in turn exacerbates catastrophic forgetting during SFT, leading to overtraining. Our findings paint a picture of the overtraining mechanism in LLMs and broadly contribute to the understanding of the interplay between optimization dynamics during pretraining and finetuning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_13627 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | (How) Learning Rates Regulate Catastrophic Overtraining Rofin, Mark Varre, Aditya Flammarion, Nicolas Machine Learning Computation and Language Supervised fine-tuning (SFT) is a common first stage of LLM post-training, teaching the model to follow instructions and shaping its behavior as a helpful assistant. At the same time, SFT may harm the fundamental capabilities of an LLM, particularly after long pretraining: a phenomenon known as catastrophic overtraining (Springer et al., 2025). To understand overtraining, we first investigate catastrophic forgetting in finetuning through the lens of implicit regularization of the learning rate. For models trained to the same SFT loss, we identify how the learning rate mediates optimization: finetuning with large and small steps converges to qualitatively different models. Next, we link forgetting to overtraining: learning rate decay increases the sharpness of the pretrained model, which in turn exacerbates catastrophic forgetting during SFT, leading to overtraining. Our findings paint a picture of the overtraining mechanism in LLMs and broadly contribute to the understanding of the interplay between optimization dynamics during pretraining and finetuning. |
| title | (How) Learning Rates Regulate Catastrophic Overtraining |
| topic | Machine Learning Computation and Language |
| url | https://arxiv.org/abs/2604.13627 |