优化器排名！高效优化器排行：提升效率必备神器大

深度解析优化器核心原理与榜单依据

〖One〗When it comes to training deep neural networks, the choice of optimizer can mean the difference between converging in hours or days—or not converging at all. This first section lays the groundwork by explaining what optimizers do and how we rank them. At its heart, an optimizer adjusts the model’s weights and biases based on the gradient of the loss function, seeking the minimum of a highdimensional, nonconvex landscape. The most fundamental algorithm is Stochastic Gradient Descent (SGD), which updates parameters in the opposite direction of the gradient. However, vanilla SGD suffers from slow convergence, sensitivity to learning rate selection, and the tendency to get stuck in saddle points. Over the years, researchers have developed dozens of variants to address these issues: momentum accelerates SGD by smoothing the update direction; AdaGrad adapts learning rates per parameter, but its accumulation of squared gradients often leads to premature decay; RMSProp solves AdaGrad’s decay problem by using a moving average; and Adam, which combines momentum with RMSProp’s adaptive learning rates, quickly became the default choice for many practitioners. The ranking criteria we use in this article include convergence speed (training epochs required to reach a target accuracy), generalization performance (how well the model performs on unseen data), memory footprint (number of additional state vectors), and robustness to hyperparameter tuning. Modern benchmarks on image classification (ImageNet), natural language processing (BERT, GPT), and reinforcement learning (Atari games) have produced a clear hierarchy: Adam and its variants (AdamW, Adamax) consistently top the charts in terms of speed and ease of use, while carefully tuned SGD with momentum remains a strong contender for achieving the best generalization, especially on larger datasets. We also consider lessknown optimizers like LAMB, used for largebatch training, and NAdam, which incorporates Nesterov momentum into Adam. By understanding the strengths and weaknesses of each, we can build a practical ranking that guides practitioners from beginners to research engineers. This foundation is crucial before diving into the specific leaders of the pack.

榜首之争：Adam系列与SGD+Momentum的巅峰对决

〖Two〗The second section zooms in on the most frequently contrasted pair in deep learning: AdamW versus SGD with Nesterov momentum. For years, SGD with carefully tuned momentum (often called SGDM) was the gold standard for achieving stateoftheart performance on vision benchmarks like ResNet on ImageNet. Its key advantage is generalization: many studies show that SGDM tends to find flatter minima, which correlates with better performance on unseen data. However, SGDM requires extensive hyperparameter search—especially learning rate scheduling (step decay, cosine annealing) and weight decay coefficient. In contrast, Adam (Adaptive Moment Estimation) is nearly turnkey: it works well with default learning rates (e.g., 1e3) and requires little tuning. But early versions of Adam had issues with convergence to sharp minima, leading to poorer generalization. The breakthrough came with AdamW, which decouples weight decay from the gradient update, effectively reintroducing SGDstyle regularization. AdamW now dominates many NLP tasks, including training of BERT and GPT models, where its speed and stability outweigh the modest generalization gap. In the latest benchmarks from the MLCommons and internal studies at major AI labs, AdamW with cosine decay achieves a win rate of over 80% against SGDM on vision tasks when using modern training recipes (e.g., ResNet50, EfficientNet). Meanwhile, a variant called FusedAdam from NVIDIA’s Apex library further reduces memory overhead by fusing operations. The runnerup in this category is NAdam (Nesterovaccelerated Adam), which often converges even faster than vanilla Adam on recurrent architectures. A special mention goes to LAMB, designed for largebatch training (up to 64K) on ImageNet, which enables linear scaling of learning rates without sacrificing accuracy. These optimizers are now integrated into every major framework (PyTorch, TensorFlow, JAX) and are the workhorses behind production models at Google, Meta, and OpenAI. For practitioners, the practical ranking is: AdamW for most tasks (especially NLP and recommendation), SGDM for vision tasks where you have the time and compute to tune heavily, and Adam for quick prototyping. The chart below (conceptual) shows that AdamW tops the “easeofuse × speed” axis, while SGDM leads the “best final accuracy” axis when optimally configured.

新兴势力与未来方向：从AdaBelief到SOAP的进化

〖Three〗The third and final section looks beyond the mainstream to emerging optimizers that promise to redefine efficiency. As hardware evolves and model sizes explode (think LLMs with trillions of parameters), the classic tradeoffs shift. One notable newcomer is AdaBelief, introduced in 2020, which modifies Adam by adapting the step size based on the belief in the gradient observation. In practice, AdaBelief achieves both fast convergence and good generalization, often beating AdamW on classification and GAN training. Another strong candidate is MADGRAD, a modified version of AdaGrad that alternates between gradient and weight updates, offering nearAdam speed with less memory. For distributed training, the Shampoo optimizer—which uses a Kroneckerfactored preconditioner—outperforms Adam on smallscale models but is computationally expensive for huge ones. A recent development from Google DeepMind is the SOAP optimizer (ShampoO with Approximate Preconditioning), which reduces Shampoo’s cost by using a hybrid approach. Meanwhile, the resurgence of signbased optimizers like SignSGD shows promise in communicationefficient settings (federated learning). In the hyperparameterfree realm, Hypergradient Descent and Auto-LR methods are still experimental but may eliminate the need for manual tuning. What does the ranking look like if we consider the latest 2024–2025 results For largescale language models (170B parameters), AdamW with a cosine schedule remains the most reliable, though Google’s PaLM used a variant with renorm. For diffusion models (e.g., Stable Diffusion, Midjourney), the DPMsolver optimizer (a separate family) outperforms standard optimizers due to its specialized step design. In reinforcement learning, the combination of Adam with gradient clipping and target networks still rules. Ultimately, the “best” optimizer is taskdependent, but the trend is clear: adaptive optimizers with careful decoupling (AdamW, AdaBelief) will continue to dominate the mainstream, while specialized optimizers for ultralarge batches (LAMB, Shampoo) will carve out niches. As a rule of thumb for readers: start with AdamW, tune its learning rate and weight decay, and only switch to SGDM if you observe a clear generalization gap. For bleedingedge research, keep an eye on SOAP and its derivatives, which might soon become the new standard in trillionparameter training. The field of optimization is far from settled—each new architecture (Mixture of Experts, statespace models) brings fresh challenges that will inevitably spawn the next generation of “purposebuilt” optimizers.

优化器排名！高效优化器排行：提升效率必备神器大

深度解析优化器核心原理与榜单依据

〖One〗When it comes to training deep neural networks, the choice of optimizer can mean the difference between converging in hours or days—or not converging at all. This first section lays the groundwork by explaining what optimizers do and how we rank them. At its heart, an optimizer adjusts the model’s weights and biases based on the gradient of the loss function, seeking the minimum of a highdimensional, nonconvex landscape. The most fundamental algorithm is Stochastic Gradient Descent (SGD), which updates parameters in the opposite direction of the gradient. However, vanilla SGD suffers from slow convergence, sensitivity to learning rate selection, and the tendency to get stuck in saddle points. Over the years, researchers have developed dozens of variants to address these issues: momentum accelerates SGD by smoothing the update direction; AdaGrad adapts learning rates per parameter, but its accumulation of squared gradients often leads to premature decay; RMSProp solves AdaGrad’s decay problem by using a moving average; and Adam, which combines momentum with RMSProp’s adaptive learning rates, quickly became the default choice for many practitioners. The ranking criteria we use in this article include convergence speed (training epochs required to reach a target accuracy), generalization performance (how well the model performs on unseen data), memory footprint (number of additional state vectors), and robustness to hyperparameter tuning. Modern benchmarks on image classification (ImageNet), natural language processing (BERT, GPT), and reinforcement learning (Atari games) have produced a clear hierarchy: Adam and its variants (AdamW, Adamax) consistently top the charts in terms of speed and ease of use, while carefully tuned SGD with momentum remains a strong contender for achieving the best generalization, especially on larger datasets. We also consider lessknown optimizers like LAMB, used for largebatch training, and NAdam, which incorporates Nesterov momentum into Adam. By understanding the strengths and weaknesses of each, we can build a practical ranking that guides practitioners from beginners to research engineers. This foundation is crucial before diving into the specific leaders of the pack.

榜首之争：Adam系列与SGD+Momentum的巅峰对决

〖Two〗The second section zooms in on the most frequently contrasted pair in deep learning: AdamW versus SGD with Nesterov momentum. For years, SGD with carefully tuned momentum (often called SGDM) was the gold standard for achieving stateoftheart performance on vision benchmarks like ResNet on ImageNet. Its key advantage is generalization: many studies show that SGDM tends to find flatter minima, which correlates with better performance on unseen data. However, SGDM requires extensive hyperparameter search—especially learning rate scheduling (step decay, cosine annealing) and weight decay coefficient. In contrast, Adam (Adaptive Moment Estimation) is nearly turnkey: it works well with default learning rates (e.g., 1e3) and requires little tuning. But early versions of Adam had issues with convergence to sharp minima, leading to poorer generalization. The breakthrough came with AdamW, which decouples weight decay from the gradient update, effectively reintroducing SGDstyle regularization. AdamW now dominates many NLP tasks, including training of BERT and GPT models, where its speed and stability outweigh the modest generalization gap. In the latest benchmarks from the MLCommons and internal studies at major AI labs, AdamW with cosine decay achieves a win rate of over 80% against SGDM on vision tasks when using modern training recipes (e.g., ResNet50, EfficientNet). Meanwhile, a variant called FusedAdam from NVIDIA’s Apex library further reduces memory overhead by fusing operations. The runnerup in this category is NAdam (Nesterovaccelerated Adam), which often converges even faster than vanilla Adam on recurrent architectures. A special mention goes to LAMB, designed for largebatch training (up to 64K) on ImageNet, which enables linear scaling of learning rates without sacrificing accuracy. These optimizers are now integrated into every major framework (PyTorch, TensorFlow, JAX) and are the workhorses behind production models at Google, Meta, and OpenAI. For practitioners, the practical ranking is: AdamW for most tasks (especially NLP and recommendation), SGDM for vision tasks where you have the time and compute to tune heavily, and Adam for quick prototyping. The chart below (conceptual) shows that AdamW tops the “easeofuse × speed” axis, while SGDM leads the “best final accuracy” axis when optimally configured.

新兴势力与未来方向：从AdaBelief到SOAP的进化

〖Three〗The third and final section looks beyond the mainstream to emerging optimizers that promise to redefine efficiency. As hardware evolves and model sizes explode (think LLMs with trillions of parameters), the classic tradeoffs shift. One notable newcomer is AdaBelief, introduced in 2020, which modifies Adam by adapting the step size based on the belief in the gradient observation. In practice, AdaBelief achieves both fast convergence and good generalization, often beating AdamW on classification and GAN training. Another strong candidate is MADGRAD, a modified version of AdaGrad that alternates between gradient and weight updates, offering nearAdam speed with less memory. For distributed training, the Shampoo optimizer—which uses a Kroneckerfactored preconditioner—outperforms Adam on smallscale models but is computationally expensive for huge ones. A recent development from Google DeepMind is the SOAP optimizer (ShampoO with Approximate Preconditioning), which reduces Shampoo’s cost by using a hybrid approach. Meanwhile, the resurgence of signbased optimizers like SignSGD shows promise in communicationefficient settings (federated learning). In the hyperparameterfree realm, Hypergradient Descent and Auto-LR methods are still experimental but may eliminate the need for manual tuning. What does the ranking look like if we consider the latest 2024–2025 results For largescale language models (170B parameters), AdamW with a cosine schedule remains the most reliable, though Google’s PaLM used a variant with renorm. For diffusion models (e.g., Stable Diffusion, Midjourney), the DPMsolver optimizer (a separate family) outperforms standard optimizers due to its specialized step design. In reinforcement learning, the combination of Adam with gradient clipping and target networks still rules. Ultimately, the “best” optimizer is taskdependent, but the trend is clear: adaptive optimizers with careful decoupling (AdamW, AdaBelief) will continue to dominate the mainstream, while specialized optimizers for ultralarge batches (LAMB, Shampoo) will carve out niches. As a rule of thumb for readers: start with AdamW, tune its learning rate and weight decay, and only switch to SGDM if you observe a clear generalization gap. For bleedingedge research, keep an eye on SOAP and its derivatives, which might soon become the new standard in trillionparameter training. The field of optimization is far from settled—each new architecture (Mixture of Experts, statespace models) brings fresh challenges that will inevitably spawn the next generation of “purposebuilt” optimizers.