AdamW Under Heavy-Tailed Noise
Reported by arXiv cs.AI: Open Problem: Is AdamW Effective Under Heavy-Tailed Noise?. Analysis and context written by TickrWire.
Researchers question the effectiveness of AdamW optimizer under heavy-tailed noise, a common scenario in large language model pretraining. A rigorous convergence theory for AdamW in this regime is still lacking.
- AdamW is the de facto optimizer for training large language models, but its theory is based on finite-variance regimes.
- Stochastic gradient noise in LLM pretraining is typically heavy-tailed, which may affect AdamW's effectiveness.
- Sign-based optimizers like Lion and Muon, and AdaGrad, have been shown to converge under heavy-tailed noise.
- A rigorous convergence theory for AdamW under heavy-tailed assumptions is still lacking.
The AdamW optimizer is widely used for training large language models, but its theoretical foundations are mostly based on finite-variance regimes. However, empirical evidence suggests that stochastic gradient noise in LLM pretraining is typically heavy-tailed. Recent studies have shown that sign-based optimizers like Lion and Muon can achieve sharp heavy-tailed rates, and AdaGrad can also converge under heavy-tailed noise. Despite this, a rigorous convergence theory for AdamW under heavy-tailed assumptions has not been established, leaving a significant gap in the understanding of this optimizer's behavior. This open problem has important implications for the development of more efficient and robust training methods for LLMs.
Understanding the limitations of AdamW under heavy-tailed noise can help developers choose the most suitable optimizer for their LLM training tasks.
Companies investing in LLM development may benefit from more efficient and robust training methods, which can lead to cost savings and improved model performance.
Investors in AI startups may be interested in the potential for new optimization techniques to improve LLM training and drive innovation in the field.
Students of machine learning and AI can gain a deeper understanding of the theoretical foundations of optimization methods and their limitations.
The development of more efficient and robust optimization methods can contribute to the advancement of AI research and its applications in various industries.
- Heavy-tailed noise
- A type of noise that has a higher probability of extreme values than traditional Gaussian noise.
- AdamW
- A popular stochastic gradient descent optimizer used for training large language models.
AI bias estimate: The article presents a neutral, technical discussion of the open problem, without expressing a personal opinion or bias. (Automated estimate, not a definitive judgement.)
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