Training Dynamics of the Cooldown Stage in Warmup-Stable-Decay Learning Rate Scheduler
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866913978606682112 |
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| author | Dremov, Aleksandr Hägele, Alexander Kosson, Atli Jaggi, Martin |
| author_facet | Dremov, Aleksandr Hägele, Alexander Kosson, Atli Jaggi, Martin |
| contents | Learning rate scheduling is essential in transformer training, where the final annealing plays a crucial role in getting the best performance. However, the mechanisms behind this cooldown phase, with its characteristic drop in loss, remain poorly understood. To address this, we provide a comprehensive analysis focusing solely on the cooldown phase in the Warmup-Stable-Decay (WSD) learning rate scheduler. Our analysis reveals that different cooldown shapes reveal a fundamental bias-variance trade-off in the resulting models, with shapes that balance exploration and exploitation consistently outperforming alternatives. Similarly, we find substantial performance variations $\unicode{x2013}$ comparable to those from cooldown shape selection $\unicode{x2013}$ when tuning AdamW hyperparameters. Notably, we observe consistent improvements with higher values of $β_2$ during cooldown. From a loss landscape perspective, we provide visualizations of the landscape during cooldown, supporting the river valley loss perspective empirically. These findings offer practical recommendations for configuring the WSD scheduler in transformer training, emphasizing the importance of optimizing the cooldown phase alongside traditional hyperparameter tuning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_01483 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Training Dynamics of the Cooldown Stage in Warmup-Stable-Decay Learning Rate Scheduler Dremov, Aleksandr Hägele, Alexander Kosson, Atli Jaggi, Martin Machine Learning Artificial Intelligence Learning rate scheduling is essential in transformer training, where the final annealing plays a crucial role in getting the best performance. However, the mechanisms behind this cooldown phase, with its characteristic drop in loss, remain poorly understood. To address this, we provide a comprehensive analysis focusing solely on the cooldown phase in the Warmup-Stable-Decay (WSD) learning rate scheduler. Our analysis reveals that different cooldown shapes reveal a fundamental bias-variance trade-off in the resulting models, with shapes that balance exploration and exploitation consistently outperforming alternatives. Similarly, we find substantial performance variations $\unicode{x2013}$ comparable to those from cooldown shape selection $\unicode{x2013}$ when tuning AdamW hyperparameters. Notably, we observe consistent improvements with higher values of $β_2$ during cooldown. From a loss landscape perspective, we provide visualizations of the landscape during cooldown, supporting the river valley loss perspective empirically. These findings offer practical recommendations for configuring the WSD scheduler in transformer training, emphasizing the importance of optimizing the cooldown phase alongside traditional hyperparameter tuning. |
| title | Training Dynamics of the Cooldown Stage in Warmup-Stable-Decay Learning Rate Scheduler |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2508.01483 |