Quadratic Models Accurately Predict LLM Pretraining Loss, Study Shows
ShamKakade6 · x · 2026-07-30
A recent study reveals that the simplest model in optimization theory, the quadratic, accurately describes LLM pretraining. By linearizing a 150M parameter model at various training checkpoints, researchers found that the local Taylor expansion tracks the true loss effectively for up to 10% of the training budget. The paper is predicted to become highly influential in the AI community.
Related event: Quadratic Models Can Accurately Describe LLM Pre-training Dynamics(3 posts)→
More from Research
- Qwen Releases Technical Report for Qwen-Audio-3.0-Gen-Preview — udmrzn · 2026-07-31
- Demystifying the Core Math Behind Large Language Models — udmrzn · 2026-07-31
- Paper Evaluates Current Tools for Detecting Hallucinated AI Citations — RexDouglass · 2026-07-31
- Study: AI Writing Detectors Have High False Negative Rates, Unreliable for Serious Use — burkov · 2026-07-31
- TMLR Submissions 4x'd: AI-Generated Junk Forces Academic Gatekeeping — thegautamkamath · 2026-07-31
- Weak-to-Strong Distillation Paper Shows Qwen3-8B Surpassing Its 4B Teacher — burny_tech · 2026-07-31