Use SGD as a baseline, then try an adaptive optimizer when its update behavior suits your training problem: Adagrad for sparse or infrequent updates, RMSprop when gradient scales vary, and Adam as a practical starting point for broad experimentation. None is guaranteed to outperform the others; compare validation performance after tuning each optimizer for your model and data.
What an optimizer does
During training, backpropagation calculates gradients that indicate how a model’s parameters contribute to its loss. An optimizer uses those gradients to change the parameters. In a typical PyTorch training loop, you clear old gradients, compute the loss and gradients, then call the optimizer’s step to update parameters. The PyTorch beginner tutorial demonstrates this workflow with SGD.
SGD is the straightforward reference point: it updates parameters using computed gradients, and PyTorch’s implementation can also use momentum. Adaptive optimizers adjust effective step sizes using gradient history. That can make them useful in particular situations, but it does not remove the need to choose a learning rate and evaluate the trained model.
How the four optimizers differ
| Optimizer | Update behavior | Reasonable time to try it | Important caveat |
|---|---|---|---|
| SGD (optionally with momentum) | Updates parameters from computed gradients; momentum is available in PyTorch’s implementation. | As a baseline, especially when you can tune and compare alternatives. | An example training loop using SGD does not mean SGD is best for every model or dataset. |
| Adagrad | Accumulates squared gradients to adapt learning rates for individual parameters. | When features or parameters are sparse, or updates happen infrequently. | Because the accumulated history grows, effective learning rates decrease over time and may impede progress in long runs. |
| RMSprop | Scales updates using a running average of recent squared-gradient magnitudes. | When gradient magnitudes vary; recurrent models are one suggested trial case. | It is a candidate to test, not a requirement for every recurrent model or a guaranteed improvement. |
| Adam | Uses adaptive learning rates and estimates of first and second gradient moments. | As a broad starting point or for quick prototyping. | It is a useful baseline to evaluate, not a guaranteed final winner. |
These are qualitative selection heuristics, not head-to-head benchmark results. PyTorch’s optimizer guidance says selection depends on architecture, dataset, and training requirements; the optimizer alias reference and stable API documentation describe the available implementations.
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#1 Best Overall
When should I use Adam instead of SGD?
Try Adam when you want a practical adaptive starting point, particularly during early prototyping or when you need a first run without extensive optimizer-specific setup. Its moment estimates let it adapt updates using gradient history. That is a reason to test it, not evidence that it will beat a tuned SGD run on your task.
Tune Adam’s learning rate and measure the metric that matters on your validation set. If the result is weaker than SGD, or its compute cost is unsuitable, keep the better-performing option for your constraints rather than defaulting to Adam by reputation.
Rank #2
- Use scikit-learn to track an example ML project end to end
- Explore several models, including support vector machines, decision trees, random forests, and ensemble methods
- Exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection
- Dive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, autoencoders, diffusion models, and transformers
- Use TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learning
Is RMSprop better than SGD?
There is no universal answer. RMSprop is worth trying when gradient magnitudes change substantially over training or across updates, since its running average emphasizes recent squared-gradient magnitudes rather than accumulating the full history. PyTorch’s guidance names recurrent models and non-stationary objectives as possible use cases.
Those examples are not a rule that every recurrent network needs RMSprop. Compare it with SGD on the same task, and select based on validation results and training cost.
Rank #3
When should I use Adagrad?
Adagrad is a reasonable trial for sparse features or parameters that are updated infrequently. Its per-parameter learning-rate adaptation reflects the squared gradients accumulated for each parameter, which can suit workloads where update frequency differs across parameters.
The same accumulation creates a trade-off: learning rates keep decreasing as more gradient history is collected. On a long training run, that can make updates too small for useful progress. Consider this limitation when choosing it for sustained training.
Rank #4
Which optimizer is best for sparse data?
Adagrad is the clearest first alternative to test when the relevant features or parameters receive sparse or infrequent updates. That is a heuristic, not a universal ranking: “sparse data” can describe different workloads, and the right choice depends on which parameters are updated and how the model performs on its target metric. Compare Adagrad with a tuned SGD baseline and, where useful, Adam.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to compare optimizers fairly
A default-setting comparison can be misleading: optimizers may respond differently to learning-rate choices. Keep the experiment focused on optimizer behavior rather than changing several training variables at once.
Best Value
- Set a baseline. Train with SGD, optionally using momentum, and record validation performance and compute cost.
- Hold the task constant. Keep the architecture, data split, preprocessing, training budget, evaluation metric, and learning-rate scheduler policy consistent across runs.
- Tune each learning rate. Give each optimizer a reasonable learning-rate search rather than comparing arbitrary defaults.
- Compare outcomes. Use the same validation metric and budget, and record compute cost alongside model quality.
- Choose for the actual requirement. Prefer the optimizer that meets your validation and practical constraints; do not infer a universal winner from one task.
PyTorch’s official documentation offers qualitative guidance, not a universal optimizer ranking or a quantified head-to-head performance claim. The result on your own model and data is the decision that matters.
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