#scaling-laws

3 articles

01 ·Inference & Serving·★ MEMBER·PAPER·16 min read SMELT explained (arXiv:2609.01343) — compute-matched looping Does running the same layers twice help once the budget is matched? SMELT holds per-token FLOPs, total parameters and KV cache within about 4%, loops the middle half twice, and reports 6.8-18.0% training-FLOPs savings on the compute-optimal frontier. 02 ·Large Language Models·★ MEMBER·PAPER·11 min read Scaling Skepticism — A Genealogy of the "Just Make It Bigger" Critique A fair accounting of the case against "more parameters, more data" — data exhaustion, the reasoning wall, and the world-model dispute. The evidence for scaling gets the same scrutiny as the evidence against it, and every claim comes with the observation that would settle it. 03 ·Large Language Models·FREE·PAPER·9 min read Scaling Laws from Scratch — Why Making Models Bigger Makes Them Smarter (and When It Doesn't) Scaling laws turn 'bigger is smarter' into an equation. This article walks from the Kaplan-era recipe to the Chinchilla paper (Hoffmann et al., 2022) — 400+ training runs, three independent estimation methods, and the 70B-vs-280B showdown — using only the paper itself as evidence.