Self-trained model switches to weekly Sunday weight updates for richer reward gradients
cephaloform · x · 2026-09-09
The author is adjusting their personal model training cadence: instead of nightly updates, the model now updates weights weekly, resting on Sundays.
The rationale is sample size: nightly runs yield only 1.2K task-level rewards (7.2K, possibly 8-10K, per week), a far more informative gradient than changing behavior in a domain based on a single sample per update. The goal is to batch multiple "large scale" tasks — like babysitting 9-hour training runs — into each weight update.
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