Revisiting Weight Drop: Beyond Regularization in Modern Deep Learning

Weight decay and ℓ2 regularization are crucial in machine learning, especially to limit network capacity and reduce irrelevant weight components. These techniques align with the principles of Occam's razor and are fundamental to discussions about the limits of generalization. However, recent studies have questioned the correlation between norm-based measures and generalization in deep networks. Although weight decay is widely used in next-generation deep networks such as GPT-3, CLIP, and PALM, its effect is still not fully understood. The emergence of new architectures such as transformers and near-epoch modeling languages ​​has further complicated the applicability of classical results to modern deep learning environments.

Efforts to understand and utilize weight loss have progressed significantly over time. Recent studies have highlighted the different effects of weight decay and ℓ2 regularization, especially for optimizers like Adam. It also highlights the influence of weight decay on optimization dynamics, including its impact on effective learning rates in scale-invariant networks. Other methods include its role in regularizing the Jacobian input and creating specific damping effects in certain optimizers. Furthermore, recent research contains the relationship between weight loss, training duration, and generalization performance. While weight decay has been shown to improve test accuracy, the improvements are typically modest, suggesting that implicit regularization plays an important role in deep learning.

Researchers from EPFL's Machine Learning Theory Laboratory have proposed a new perspective on the role of weight decay in modern deep learning. Their work challenges the traditional view of weight decay as primarily a regularization technique, as studied in classical learning theory. They have shown that weight decay significantly modifies the optimization dynamics in overparameterized and underparameterized networks. Furthermore, decreasing weight prevents sudden loss of divergence in bfloat16 mixed precision training, a crucial aspect of LLM training. It is applied on various architectures, from ResNets to LLM, indicating that the main advantage of weight decay lies in its ability to influence training dynamics rather than acting as an explicit regularizer.

The experiments are carried out by training GPT-2 models in OpenWebText using the NanoGPT repository. A 124M parameter model (GPT-2-Small) trained for 50,000 iterations is used, with modifications to ensure practicality within academic constraints. It is found that the training and validation losses remain closely aligned between different weight decay values. The researchers propose two main mechanisms for weight loss in LLMs:

• Improved optimization, as seen in previous studies.
• Preventing loss divergence when using bfloat16 precision.

These findings contrast with data-limited environments where generalization is the key focus, highlighting the importance of optimization speed and training stability in LLM training.

Experimental results reveal a crucial effect of weight decay in enabling stable mixed-precision bfloat16 training for LLM. Bfloat16 training speeds up the process and reduces GPU memory usage, allowing for larger models and larger batch sizes to be trained. However, even the most stable bfloat16 can exhibit late training spikes that impair model performance. It has also been proven that weight loss prevents these divergences. While float16 training is known to encounter problems with moderately large values ​​exceeding 65,519, it poses a different challenge and its limited accuracy can lead to problems when adding network components with different scales. Weight loss effectively solves these precision-related problems by preventing excessive weight growth.

In this article, researchers presented a new perspective on the role of weight decay in modern deep learning. They concluded that weight loss shows three distinct effects on deep learning:

• Provide regularization when combined with stochastic noise.
• Improved training loss optimization
• Ensure stability in low precision training.

Researchers are challenging the traditional idea that weight loss acts primarily as an explicit regularizer. Instead, they argue that its widespread use in modern deep learning is due to its ability to create beneficial changes in optimization dynamics. This view offers a unified explanation for the success of weight decay across different architectures and training environments, ranging from vision tasks with ResNets to LLM. Future approaches include model training and hyperparameter tuning in the field of deep learning.

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Sajjad Ansari is a final year student of IIT Kharagpur. As a technology enthusiast, he delves into the practical applications of ai with a focus on understanding the impact of ai technologies and their real-world implications. Its goal is to articulate complex ai concepts in a clear and accessible way.