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Use SGD as a baseline, then try Adagrad when updates are sparse or infrequent, RMSprop when gradient scales vary, and Adam when you want an adaptive starting point for a broad range of tasks. None is universally best: compare tuned runs on the validation metric that matters for your model and dataset.
What an optimizer does
During training, backpropagation computes gradients that indicate how the loss changes with model parameters. An optimizer uses those gradients to update the parameters. In PyTorch’s basic workflow, you clear old gradients, compute the loss and its gradients, then call the optimizer’s step to update the model. The official beginner tutorial demonstrates this process with SGD.
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SGD is a useful comparison point, not a promise of the best result. PyTorch’s tutorial notes that other optimizers may work better for different models and data. Its optimizer guidance likewise says selection depends on architecture, dataset, and training requirements.
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Adaptive optimizers adjust effective step sizes across parameters using information from gradient history. The methods differ in what history they keep and how they use it:
#1 Best Overall
| Optimizer | What distinguishes it | Reasonable trial condition | Caveat |
|---|---|---|---|
| SGD, optionally with momentum | Updates parameters using gradients; PyTorch’s implementation also offers momentum. | Keep it as a baseline, especially when you have time to tune and compare. | An official tutorial example is not evidence that SGD is best for every model or dataset. |
| Adagrad | Accumulates squared gradients to adapt learning rates for individual parameters. | Sparse features or parameters that are updated infrequently. | Accumulated history makes learning rates decrease over time, which can hinder progress in a long run. |
| RMSprop | Scales updates using a running average of recent squared-gradient magnitudes. | Objectives with varying gradient scales; recurrent models are one suggested use case. | This is a candidate to test, not a rule that recurrent models require RMSprop. |
| Adam | Uses adaptive learning rates and estimates of the first and second moments of gradients. | A broad starting point or quick prototyping. | A convenient starting point is not a guarantee that Adam will produce the best validated result. |
These are qualitative selection heuristics in PyTorch’s optimizer guidance, not head-to-head benchmark results. The official optimizer alias reference lists the corresponding optimizer implementations.
When should I use Adam instead of SGD?
Try Adam when you want an adaptive optimizer to get an initial training run moving, particularly during quick prototyping. It adjusts updates using gradient history, which can make it a practical starting point across varied tasks. Still tune its learning rate and compare validation performance with a tuned SGD baseline; default settings alone do not establish a fair comparison.
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?
Not in general. RMSprop is worth testing when gradient magnitudes change over time or across the objective; PyTorch’s guidance also names recurrent models as a possible fit. Whether it beats SGD depends on the particular workload, learning-rate choices, and evaluation metric. The source guidance does not establish a universal ranking.
When should I use Adagrad?
Consider Adagrad when features or parameters receive sparse or infrequent updates. Its per-parameter learning rates reflect accumulated squared gradients, making that update pattern a relevant reason to try it. The same accumulation causes learning rates to decrease over time, so it may become harder to make progress during long training runs.
Rank #3
Which optimizer is best for sparse data?
Adagrad is the most directly supported first trial among these choices when sparsity means that some features or parameters are updated infrequently. That is a reason to test it, not a guarantee: measure the validation metric on your actual task. If the data are described as sparse for another reason, the label alone does not determine which optimizer will work best.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to compare optimizers fairly
Change the optimizer, not the rest of the experiment. Keep the architecture, data split, preprocessing, training budget, evaluation metric, and scheduler policy consistent. Tune each optimizer’s learning rate rather than comparing arbitrary defaults, then record both validation results and compute cost.
Rank #4
- Choose the validation metric that reflects your task and fix the data split and preprocessing.
- Set a consistent training budget and scheduler policy for the comparison.
- Run SGD, optionally with momentum, as a baseline and tune its learning rate.
- Choose adaptive candidates based on the update pattern: Adagrad for sparse or infrequent updates, RMSprop for changing gradient scales, and Adam as a broad starting point.
- Tune each candidate’s learning rate, compare validation outcomes, and record compute cost. Keep the optimizer that performs best for your requirements rather than assuming one family wins universally.
The PyTorch stable optimizer reference reports a last update of May 10, 2026. These official materials offer practical qualitative guidance; they do not supply a quantified head-to-head result that would settle the choice for every workload.
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