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 with Group Relative Policy Optimization (GRPO), it is essential to recognize the distinct contexts in which these algorithms operate. AdamW, a variant of the Adam optimizer, is widely used in deep learning for its adaptive learning rate capabilities and weight decay regularization, which helps in achieving better generalization (Loshchilov & Hutter, 2017). Its efficiency and effectiveness in training neural networks have made it a go-to choice for many practitioners. On the other hand, GRPO is a reinforcement learning algorithm designed to optimize policies in a way that accounts for the relative performance of groups of policies rather than individual policies. This approach allows for more robust exploration of the policy space and can lead to improved sample efficiency (Schulman et al., 2017). GRPO focuses on maximizing the expected reward while ensuring that the updates to the policy maintain a certain level of stability, which is crucial in environments with high variance. While both AdamW and GRPO aim to optimize performance, they cater to different domains: AdamW is primarily for supervised learning tasks, whereas GRPO is tailored for reinforcement learning scenarios. The choice between these two methods ultimately depends on the specific problem at hand. For tasks involving static datasets and supervised learning, AdamW is likely the superior choice due to its proven track record. Conversely, for dynamic environments where learning from interaction is key, GRPO presents a compelling option that emphasizes policy robustness and adaptability. In conclusion, while AdamW and GRPO serve different purposes within the machine learning landscape, both contribute significantly to their respective fields. Understanding their strengths and limitations is crucial for practitioners looking to leverage these optimizers effectively.

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

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Group Relative Policy Optimization (GRPO)

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