Superpositional Gradient Descent Achieves Faster Convergence And Lower Loss Than AdamW In Large Language Model Training

Scaling Laws: How to Allocate Compute for Training Language Models

Author(s): M Originally published on Towards AI. From Chinchilla’s 20:1 rule to SmolLM3’s 3,700:1 ratio: how inference economics rewrote the training playbook Training a language model is expensive. Really expensive. A single training run for a 70 billion parameter model can cost millions of dollars in compute. The first content image illustrating scaling laws in model training.This article explores the concept of scaling laws in training langua…

Superpositional Gradient Descent Achieves Faster Convergence And Lower Loss Than AdamW In Large Language Model Training

Researchers demonstrate that a new optimisation technique, inspired by the principles of quantum superposition, accelerates the training of large language models and achieves improved performance compared to conventional methods.