Segment 47
In this sub-session, the assistant focused on monitoring the DFlash training pipeline running at 16 Ktok/s with healthy convergence (loss ~1.4, accuracy ~0.17, estimated acceptance length ~3.6). The user proposed switching from AdamW to the Muon optimizer to potentially accelerate convergence. The assistant provided a detailed analysis recommending against the change, citing the target model forward pass as the bottleneck (not optimizer speed), the loss of Adam's momentum/variance state mid-training, the untested GDN layers with Muon's orthogonalization, and diminishing returns over long 6-epoch schedules. The decision was to stay with AdamW and let the current run complete, deferring optimizer experiments to future training rounds.
The Discipline of Inaction: A Strategic Decision in the DFlash Training Pipeline