Tasks, stability, architecture, and compute: Training more effective learned optimizers, and using them to train themselves (2009.11243v1)

Abstract: Much as replacing hand-designed features with learned functions has revolutionized how we solve perceptual tasks, we believe learned algorithms will transform how we train models. In this work we focus on general-purpose learned optimizers capable of training a wide variety of problems with no user-specified hyperparameters. We introduce a new, neural network parameterized, hierarchical optimizer with access to additional features such as validation loss to enable automatic regularization. Most learned optimizers have been trained on only a single task, or a small number of tasks. We train our optimizers on thousands of tasks, making use of orders of magnitude more compute, resulting in optimizers that generalize better to unseen tasks. The learned optimizers not only perform well, but learn behaviors that are distinct from existing first order optimizers. For instance, they generate update steps that have implicit regularization and adapt as the problem hyperparameters (e.g. batch size) or architecture (e.g. neural network width) change. Finally, these learned optimizers show evidence of being useful for out of distribution tasks such as training themselves from scratch.

• The paper introduces a hierarchical neural optimizer that integrates validation loss for automatic regularization and enhanced generalization.
• The paper demonstrates that training on thousands of diverse tasks enables the learned optimizer to rival traditional methods without extensive hyperparameter tuning.
• The paper reveals a novel self-optimization approach where learned optimizers are used to train new optimizers, supporting recursive meta-learning improvements.

Overview of "Tasks, stability, architecture, and compute: Training more effective learned optimizers, and using them to train themselves"

The paper "Tasks, stability, architecture, and compute: Training more effective learned optimizers, and using them to train themselves" explores the development of learned optimizers capable of generalizing across a broad array of tasks with minimal user-supplied hyperparameter tuning. By leveraging large computing resources and diverse training task distributions, which includes thousands of optimization tasks, the research aims to overcome previously identified barriers such as computational scale and architectural biases in the domain of learned optimizers.

Key Contributions and Findings

1. Hierarchical Optimizer Architecture: The paper introduces a novel neural network-based hierarchical optimizer architecture that incorporates additional features like validation loss to facilitate automatic regularization. This architecture is distinguished by its ability to generalize better than previously existing architectures, which typically were trained on fewer tasks and required substantial expert pruning and tuning.
2. Scale and Diversity in Training: The training process involves using a significantly larger dataset of optimization tasks derived from the TaskSet dataset, representing various machine learning paradigms. This diverse foundational training set is critical for improving the generalization ability of the optimizers.
3. Empirical Evaluations Against Hand-Designed Optimizers: The paper presents extensive evaluations comparing their learned optimizers to traditional hand-designed optimizers such as Adam and NAdam with fixed and tuned hyperparameters. In scenarios with limited hyperparameter tuning ('off-the-shelf'), the learned optimizer achieves comparable or superior performance, demonstrating its utility without the need for problem-specific tuning.
4. Implicit Regularization Effects: One of the interesting behaviors observed in the learned optimizer is its ability to perform implicit regularization. This is evidenced by its tendency to guide trajectories in parameter space toward solutions with smaller norms, thereby working similarly to a regularizer that encourages less complex solutions.
5. Self-Optimization: A noteworthy contribution is the demonstration of using the learned optimizer to self-train new instances of learned optimizers from scratch. This property indicates a form of meta-generalization that suggests potential for recursive improvement—akin to the 'self-hosting' compilers concept.

Implications and Future Directions

• Theoretical Insights: The paper supports the hypothesis that as diverse tasks are used to train learned optimizers, their generalization capability improves, which provides significant insights into how learned optimizers can be designed and trained for broader applicability.
• Computational Resources: While the presented methods showcase significant advancement in capability, they concomitantly require substantial computational resources, approximately 5,000 CPU years for training. It suggests an imperative need to refine outer-training efficiencies to make these improvements more environmentally sustainable.
• Application Scope: The practical applicability of these learned optimizers extends to scenarios where hyperparameter tuning is resource-intensive, potentially democratizing access to optimization expertise via pre-trained optimizers.
• Architectural and Feature Insights: By capitalizing on neural network-based parameterizations and intelligently exploiting additional features such as validation losses, further advancements and understandings in the field of meta-learning and optimization could be realized.

The research carried out extends the potential of learned optimizers, opening doors for adaptive, efficient, and general-purpose learning algorithms which could significantly alter the landscape of model training and parameter optimization in machine learning. Future research could focus on reducing the computational intensity and further exploring the bounds of outer-generalization.