v8 = capacity-axis A/B: freeze the v6/v7 2.255B FineWeb-edu subset, scale
dim768→dim1024 (core 127M→226M, +78%) via bf16 + T13 activation recompute.
8-GPU DDP, 2.36B tok (1.05 ep), ~129K tok/s (recompute tax), ~5h.
Result (same FineWeb val, v6/v7/v8 comparable): v6 3.0652 / v7 3.0149 /
v8 2.9801. Capacity helps — v8 (1.05ep) beats v6 at the same ~1ep by 0.085
AND beats v7 (smaller model, 1.45ep more old data) by 0.035 ⇒ v6/v7 were
partly capacity-limited, scaling capacity > repeating old data. But the gain
is only ~3% (same magnitude as the data-axis single-step lever), and v8's
val was still descending at the end (not saturated).
Meta-finding: every single-axis lever (data-volume v5/v7, breadth v6,
capacity v8) is now ~3%/lever ⇒ broad diminishing returns; to progress,
scale capacity AND data together (Chinchilla, reproduced at toy scale).
- docs/runs/08-v8-fineweb-edu-dim1024.md: full capacity experiment + v7-vs-v8 samples
- docs/runs/README.md: +v8 row, v9 proposal
- docs/evolution.md: +T13 infra row, +v8 scaling row, capacity-axis & diminishing-returns notes
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
