4 points | by Anon84 an hour ago
3 comments
Data is doing more of the work than it used to. Every source in our mixture is a curated artifact built with large models
Training a model this small on them is distillation
When models of this size were last studied seriously such corpora did not exist
The loss does not saturate. Across a 4.91B-token run, smoothed training loss falls monotonically within each curriculum phase and is still descending at the end
blog: https://gregdiamos.com/2026/09/07/outrageously-small-neural-...
X discussion: https://x.com/GregoryDiamos/status/2096873745420075020?s=20
I added some of the main points to the thread so they are easier to read.
Data is doing more of the work than it used to. Every source in our mixture is a curated artifact built with large models
Training a model this small on them is distillation
When models of this size were last studied seriously such corpora did not exist
The loss does not saturate. Across a 4.91B-token run, smoothed training loss falls monotonically within each curriculum phase and is still descending at the end
blog: https://gregdiamos.com/2026/09/07/outrageously-small-neural-...
X discussion: https://x.com/GregoryDiamos/status/2096873745420075020?s=20
I added some of the main points to the thread so they are easier to read.