Practical guidance for choosing which optimizer to actually use — synthesizing the full comparison table from AdamW into direct, actionable recommendations.
The Practical Default
For the large majority of modern deep learning tasks, AdamW is a reasonable, well-tested default starting point — it combines adaptive per-parameter learning rates, momentum, and correctly-behaved weight decay, requiring comparatively little tuning to get solid results across a wide range of architectures and tasks.
When to Consider Alternatives
| Situation | Consider | Why |
|---|---|---|
| Some computer vision tasks (especially large-scale image classification) | SGD with momentum | Well-tuned SGD with momentum and a good learning rate schedule has, in some published results, generalized slightly better than Adam-family optimizers for certain CV benchmarks, at the cost of needing more careful tuning |
| RNN/LSTM-based architectures | RMSProp (or Adam) | RMSProp was historically a strong choice for recurrent architectures specifically, though Adam has largely become the more common modern default here too |
| Very large-scale LLM pretraining | AdamW (near-universal) | The near-universal standard for this specific regime, given its combination of correct weight decay and adaptive scaling at scale |
A Practical Decision Process
- Start with AdamW using its typical default hyperparameters (\(\beta_1=0.9, \beta_2=0.999\)) and a learning rate from Learning Rate Tuning's typical range.
- If results are reasonable but you suspect a better generalization ceiling might exist (common in some CV settings), experiment with SGD + momentum + a learning rate schedule as a follow-up comparison.
- For anything closely following an established published architecture/recipe, matching its documented optimizer choice is often a reasonable, well-validated starting point rather than searching from scratch.
Code
import torch.optim as optim
# The practical default for most tasks
optimizer = optim.AdamW(model.parameters(), lr=1e-3, weight_decay=0.01)
# A common alternative worth comparing for some CV tasks
optimizer_alt = optim.SGD(model.parameters(), lr=0.1, momentum=0.9, weight_decay=1e-4)
scheduler_alt = optim.lr_scheduler.CosineAnnealingLR(optimizer_alt, T_max=100)Common Mistakes
- Assuming one optimizer is universally, provably superior across every single task and architecture — the choice genuinely has task-dependent nuance, and empirical validation on your specific setup remains the ultimate deciding factor.
- Switching optimizers frequently mid-project without a clear, deliberate reason — each optimizer has different characteristic training dynamics, and frequent switching makes it hard to build reliable intuition about what's actually driving observed performance changes.
Interview Relevance
Q: "If you're starting a new deep learning project with no strong prior, which optimizer would you reach for first, and why?" AdamW — it combines adaptive per-parameter learning rates, momentum, and correctly-behaved weight decay, and empirically performs reasonably well "out of the box" across a very wide range of architectures and tasks with comparatively little tuning required, making it a sensible, low-risk starting point before considering task-specific alternatives.
Practice Question
For a task where you're closely reproducing a published research paper's results, what optimizer choice would you likely start with, and why?
