Alternatives to AdamW optimizer

At a glance

Executive summary

AdamW is an optimization algorithm that decouples weight decay from the adaptive learning rate mechanism of Adam. This separation helps to prevent the optimizer from incorrectly decaying weights, leading to improved generalization and performance, especially in deep learning models.

TL;DR

If you need a robust, general-purpose optimizer for deep learning, use AdamW; if you are working with sequential decision-making problems, consider reinforcement learning techniques like GRPO.

Key points

AdamW is a strong default choice for most deep learning tasks due to its improved generalization.
Reinforcement learning is a broad field focused on agents learning through trial and error, not a direct optimizer alternative to AdamW.
GRPO is a specific policy optimization algorithm within RL, designed for stable and efficient policy updates in complex environments.
Consider AdamW when your primary goal is supervised or unsupervised learning with standard neural networks.
Choose GRPO (or other RL algorithms) when your problem involves an agent interacting with an environment to learn optimal actions.

Our Take

### Our Take When comparing the AdamW optimizer and Group Relative Policy Optimization (GRPO), it's essential to recognize that they serve distinct purposes in the machine learning landscape. AdamW, an extension of the Adam optimizer, introduces weight decay directly into the optimization process, leading to improved generalization in deep learning models (Loshchilov & Hutter, 2017). This method has gained traction for its efficiency in handling sparse gradients and adapting the learning rate dynamically, making it a preferred choice for training neural networks. On the other hand, GRPO is a reinforcement learning algorithm designed to optimize policies in a way that maintains a balance between exploration and exploitation. It focuses on group-based learning, allowing multiple agents to learn from each other's experiences while minimizing the variance in policy updates (TAMER, 2016). This collaborative approach can lead to more robust policy learning, especially in complex environments where individual agents may struggle to converge. While AdamW excels in the realm of supervised learning tasks, providing a solid foundation for training deep networks, GRPO shines in scenarios requiring adaptive decision-making under uncertainty. The two methodologies, therefore, cater to different aspects of machine learning. AdamW is about optimizing model parameters efficiently, while GRPO emphasizes the importance of collective learning in dynamic environments. In conclusion, the choice between AdamW and GRPO ultimately depends on the specific challenges at hand. For tasks focused on model training, AdamW is a reliable optimizer, whereas GRPO is better suited for reinforcement learning scenarios where agent collaboration is key. Understanding their unique strengths allows practitioners to leverage the right tool for their specific needs.

Alternative	Difference	Papers (with AdamW optimizer)	Avg viability
reinforcement learning	—	1	—
Group Relative Policy Optimization (GRPO)	—	1	—

Alternative

Difference

Papers (with AdamW optimizer)

Avg viability

reinforcement learning

—

Group Relative Policy Optimization (GRPO)

—