Adaptive Learning Rate Schedule Alternatives And Similar Packages

deps.dev - Dependencies and security vulnerabilities Gitingest - Export source code for LLM usage Repomix - Export source code for LLM usage uithub - Export source code for LLM usage Users that are interested in adaptive-learning-rate-schedule are comparing it to the libraries listed below The learning rate is a crucial hyperparameter in deep learning, with its ideal value depending on the problem and potentially changing during training.

In this paper, we investigate the practical utility of adaptive learning rate mechanisms that adjust step sizes dynamically in response to the loss landscape. We revisit a cumulative path-based adaptation scheme proposed in 2017, which adjusts the learning rate based on the discrepancy between the observed path length, computed as a time-discounted sum of normalized gradient steps, and... While the original approach offers a compelling intuition, we show that its adaptation mechanism for Adam is conceptually inconsistent due to the optimizer’s internal preconditioning. We propose a corrected variant that better reflects Adam’s update dynamics. To assess the practical value of online learning rate adaptation, we benchmark SGD and Adam, with and without cumulative adaptation, and compare them to a recent alternative method. Our results aim to clarify when and why such adaptive strategies offer practical benefits.

Gradient-based optimization underpins modern machine learning, powering progress in domains ranging from image recognition to reinforcement learning [6, 1, 19]. Among the available methods, Stochastic Gradient Descent (SGD) remains a cornerstone due to its simplicity and scalability [1]. However, its performance is highly sensitive to the choice of hyperparameters, particularly the learning rate. An inappropriate learning rate can slow convergence, cause divergence, or optimize traps in poor regions of the loss landscape [18]. Adaptive optimizers such as AdaGrad [4], RMSProp [10], and Adam [12] aim to mitigate these issues by scaling updates based on past gradient information. Adam, in particular, has become a default choice due to its strong empirical performance and relatively low sensitivity to gradient scale and initialization.

Like other adaptive methods, it relies on a global learning rate whose choice remains critical [12, 6, 17]. In practice, this often necessitates heuristic schedules or exhaustive tuning. Despite decades of research, learning rate selection remains one of the most impactful and under-specified aspects of training deep models [20]. Manual tuning or fixed schedules are widely used, but these approaches are brittle: they are expensive to tune, often problem-specific, and can reduce reproducibility across architectures and datasets [7, 17]. In this paper, we explore an alternative approach through Cumulative Learning Rate Adaptation (CLARA), a lightweight mechanism that adjusts the global learning rate on the fly by analyzing the optimizer’s trajectory. Rather than relying on instantaneous gradient magnitudes or predefined schedules, CLARA leverages the cumulative directionality of recent updates to infer whether current steps are consistently aligned or conflicting, guiding the learning rate accordingly.

A Gentle Introduction to Learning Rate SchedulersImage by Author | ChatGPT Ever wondered why your neural network seems to get stuck during training, or why it starts strong but fails to reach its full potential? The culprit might be your learning rate – arguably one of the most important hyperparameters in machine learning. While a fixed learning rate can work, it often leads to suboptimal results. Learning rate schedulers offer a more dynamic approach by automatically adjusting the learning rate during training. In this article, you’ll discover five popular learning rate schedulers through clear visualizations and hands-on examples.

You’ll learn when to use each scheduler, see their behavior patterns, and understand how they can improve your model’s performance. We’ll start with the basics, explore sklearn’s approach versus deep learning requirements, then move to practical implementation using the MNIST dataset. By the end, you’ll have both the theoretical understanding and practical code to start using learning rate schedulers in your own projects. Imagine you’re hiking down a mountain in thick fog, trying to reach the valley. The learning rate is like your step size – take steps too large, and you might overshoot the valley or bounce between mountainsides. Take steps too small, and you’ll move painfully slowly, possibly getting stuck on a ledge before reaching the bottom.

A long long time ago, almost all neural networks were trained using a fixed learning rate and the stochastic gradient descent (SGD) optimizer. Then the whole deep learning revolution thing happened, leading to a whirlwind of new techniques and ideas. In the area of model optimization, the two most influential of these new ideas have been learning rate schedulers and adaptive optimizers. In this chapter, we will discuss the history of learning rate schedulers and optimizers, leading up to the two techniques best-known among practitioners today: OneCycleLR and the Adam optimizer. We will discuss the relative merits of these two techniques. TLDR: you can stick to Adam (or one of its derivatives) during the development stage of the project, but you should try additionally incorporating OneCycleLR into your model as well eventually.

All optimizers have a learning rate hyperparameter, which is one of the most important hyperparameters affecting model performance. Created On: Jun 13, 2025 | Last Updated On: Aug 24, 2025 torch.optim is a package implementing various optimization algorithms. Most commonly used methods are already supported, and the interface is general enough, so that more sophisticated ones can also be easily integrated in the future. To use torch.optim you have to construct an optimizer object that will hold the current state and will update the parameters based on the computed gradients. To construct an Optimizer you have to give it an iterable containing the parameters (all should be Parameter s) or named parameters (tuples of (str, Parameter)) to optimize.

Then, you can specify optimizer-specific options such as the learning rate, weight decay, etc. Adaptive Optimization in Machine Learning is a set of techniques that automatically adjust the learning rate during training. Unlike traditional methods like basic SGD that use a fixed learning rate adaptive optimizers like Adam, RMSprop, Adagrad change the learning rate for each parameter based on the data and gradient history. This makes training faster, more stable and often easier especially for deep learning tasks and models with complex or sparse data. Adaptive optimization refers to a class of optimization algorithms that automatically modify learning rates based on the characteristics of the data and gradients. These optimizers aim to:

They do this by maintaining and updating internal state variables allowing them to scale the updates differently for each parameter. PyTorch implementation of the "Learning an Adaptive Learning Rate Schedule" paper found here: https://arxiv.org/abs/1909.09712. Work in progress! A controller is optimized by PPO to generate adaptive learning rate schedules. Both the actor and the critic are MLPs with 2 hidden layers of size 32. Three distinct child network architectures are used: 1) an MLP with 3 hidden layers, 2) LeNet-5 and 3) ResNet-18.

Learning rate schedules are evaluated on three different datasets: 1) MNIST, 2) Fashion-MNIST and 3) CIFAR10. Original paper experiments with combinations of Fashion-MNIST, CIFAR10, LeNet-5 and ResNet-18 only. In each of the three settings, child networks are optimized using Adam with an initial learning rate in (1e-2, 1e-3, 1e-4) and are trained for 1000 steps on the full training set (40-50k samples)... 20-25 epochs. Learning rate schedules are evaluated based on validation loss over the course of training. Test loss and test accuracies are in the pipeline.

Experiments are made in both a discrete and continuous setting. In the discrete setting, the controller controls the learning rate by proposing one of the following actions every 10 steps: 1) increase the learning rate, 2) decrease the learning rate, 3) do nothing. In the continuous setting, the controller instead proposes a real-valued scaling factor, which allows the controller to modify learning rates with finer granularity. Maximum change per LR update has been set to 5% for simplicity (action space is not stated in the paper). In both the discrete and the continuous setting, Gaussian noise is optionally applied to learning rate updates. Observations for the controller contain information about current training loss, validation loss, variance of predictions, variance of prediction changes, mean and variance of the weights of the output layer as well as the previous...

To make credit assignment easier, the validation loss at each step is used as reward signal rather than the final validation loss. Both observations and rewards are normalized by a running mean. In machine learning, the learning rate determines the size of steps the model takes to minimize error during training. A high learning rate can cause instability by overshooting the optimal solution, while a low rate may lead to slow convergence or getting stuck in suboptimal solutions. That’s why a learning rate schedule is typically used to adjust the rate over time during training. Early in training, a higher learning rate helps the model learn quickly and capture general patterns.

As training progresses, the rate is reduced to fine-tune and converge to an optimal solution. Common schedules include step decay, exponential decay, and adaptive methods, each aiding in efficient and accurate training. Key hyperparameters include schedule type (e.g., linear or cosine), warmup steps, and weight decay, which often require careful tuning. Although these schedules generally yield satisfactory results, they can still be suboptimal. Schedule-free alternatives exist for popular optimizers but remain underexplored for fine-tuning large language models (LLMs). The Kaitchup – AI on a Budget is a reader-supported publication.

To receive new posts and support my work, consider becoming a free or paid subscriber. In this article, we will first explore how schedule-free optimizers work and why, in theory, they can outperform their schedule-based counterparts. Next, we will experiment with a schedule-free AdamW in a common training scenario: fine-tuning Llama 3.2 with LoRA for chat applications. We will show that while schedule-free AdamW can indeed surpass the performance of traditional AdamW for LLM fine-tuning, it also comes with certain drawbacks that can make it difficult to use in some configurations. Okay, let's break down learning rate schedules and why they're a crucial part of training neural networks, especially when combined with adaptive optimizers. A learning rate schedule is a strategy for adjusting the learning rate during the training process of a machine learning model, particularly a neural network.

The learning rate is a hyperparameter that controls the step size taken during each iteration of gradient descent. It essentially determines how much the model's weights are updated based on the calculated error. Here are some popular types of learning rate schedules: Why are Learning Rate Schedules Used with Adaptive Optimizers? This is where it gets really interesting. Adaptive optimizers (like Adam, RMSprop, Adagrad, etc.) are designed to automatically adjust the learning rate for each parameter in the model.

They do this based on the history of gradients for that specific parameter. However, even with adaptive optimizers, a learning rate schedule can still be beneficial.

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