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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsNeither optimizer wins for every fine-tuning task. In a Microsoft Research comparison of modern vision models, AdamW substantially outperformed SGD on the tested downstream tasks, especially under distribution shift. But freezing the models’ small embedding layer changed the result: SGD performed slightly better in the study’s tested settings and used less optimizer-state memory. These findings are specific to the architectures and tasks evaluated, not a universal rule for all fine-tuning.
First, distinguish Adam from AdamW
The most directly relevant fine-tuning comparison is between SGD and AdamW, not vanilla Adam. AdamW uses decoupled weight decay: the weight-decay update is separated from Adam’s adaptive gradient update. That distinction matters because optimizer results depend on the method being tested, not just the broad optimizer family.
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The decoupled weight-decay paper reports improved Adam generalization in its image-classification experiments and says the approach can compete with momentum SGD. That evidence helps explain why AdamW is a useful comparator, but it does not establish that AdamW always beats SGD. Loshchilov and Hutter, “Decoupled Weight Decay Regularization”.
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What the direct fine-tuning comparison found
Microsoft Research’s “How to Fine-Tune Vision Models with SGD”, listed as an ICLR 2024 publication, compares SGD and AdamW when fine-tuning modern Vision Transformer and ConvNeXt models. The authors report that AdamW substantially outperformed SGD across their downstream-task suite, with particularly large gaps on tasks involving distribution shifts.
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The study also connects the optimizer gap to unusually large gradients in the models’ first embedding layer. The authors tested freezing that layer, which represented less than 1% of parameters in their analysis. In those settings, SGD—with or without momentum—performed slightly better than AdamW across the datasets and models they tested.
The authors report state-of-the-art accuracies on five distribution-shift benchmarks: WILDS-FMoW, WILDS-Camelyon, BREEDS-Living-17, Waterbirds, and DomainNet. Those results describe the paper’s evaluated vision models and tasks; they do not settle which optimizer is better for language models or other fine-tuning domains.
How optimizer-state memory compares
In the case where the methods perform the same, the Microsoft Research authors report these optimizer-memory figures:
| Method | Reported optimizer memory |
|---|---|
| SGD with momentum | 12 bytes per parameter |
| SGD without momentum | 8 bytes per parameter |
| AdamW | 16 bytes per parameter |
These are the study authors’ stated figures for optimizer memory, not a complete estimate of training memory. Actual total memory use can also depend on implementation, numeric precision, model activations, gradients, and other training choices. The practical point is conditional: when performance is equivalent, SGD’s lower reported optimizer-state cost may make it attractive under a memory constraint.
How to choose fairly for your fine-tuning task
Do not decide from optimizer defaults alone. Optimizer comparisons can change with the hyperparameter-tuning protocol, and each method needs an appropriate configuration. The empirical-comparisons paper discusses how tuning choices affect conclusions; “On Empirical Comparisons of Optimizers for Deep Learning”.
- Define the target. Record the model, dataset, fine-tuning procedure, validation metric, and whether the target data may differ from the pretraining distribution. A ranking on one task does not automatically transfer to another.
- Compare AdamW with SGD as distinct methods. State whether SGD uses momentum, and record weight decay rather than treating “Adam” and “AdamW” as interchangeable labels.
- Tune each optimizer’s settings. Give both methods suitable learning rates, schedules, and regularization settings. If trials are limited, Google’s tuning guide recommends prioritizing Adam’s base learning rate and using a decaying learning-rate schedule rather than a constant one. These are tuning recommendations, not a substitute for testing on your task: Google’s learning-rate tuning guide.
- Keep the comparison budget and evaluation consistent. Use the same model, data split, validation metric, training budget, and stopping rule; disclose the tuning trial budget and final settings so readers can interpret the result.
- Account for architecture and resource limits. If using a comparable vision architecture and memory is tight, test whether freezing the embedding layer is viable. Treat it as a targeted experiment suggested by the study, not a guaranteed improvement for other models.
- Choose using both quality and cost. Compare validation performance alongside optimizer-state memory and training constraints. If one method is better on the metric that matters and fits available resources, that is the relevant winner for your use case.
What can—and cannot—be concluded
For the modern vision-model fine-tuning settings studied by Microsoft Research, AdamW is the stronger starting point when the embedding layer remains trainable, particularly for distribution-shift tasks. Freezing that layer reversed the slight performance preference in the study, favoring SGD, while its reported optimizer-memory figures were lower. No evidence here establishes one optimizer as best across all models, datasets, or fine-tuning domains.
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