Chunk 50.1

This chunk focused on implementing the bucketed shuffle strategy and launching the corrected production training run. The assistant deployed the analytically optimized bucket boundaries (`[0, 770, 1216, 1728, 2432, 3296, 8192]`) into the training pipeline, copied the updated script to the kpro6 container, and restarted the run from scratch. The new strategy achieved a steady-state throughput of **25.1 Ktok/s** with a **5.1-day ETA**, successfully recovering from the disastrous 12 Ktok/s of the full random shuffle. While this is ~78% of the original sorted throughput (slightly below the 87% padding efficiency estimate due to the overhead of variable batch sizes on model execution), the pipeline is perfectly balanced and producing diverse batches each epoch. This confirmed the core trade-off between pure computational efficiency and gradient diversity was successfully optimized. When the user shared a W&B screenshot showing a "jumpy" loss curve, the assistant diagnosed this as a healthy indicator of the new strategy working correctly—consecutive batches now draw from different length buckets, causing natural oscillation in loss values, which is a deliberate improvement over the artificially smooth but flawed sorted method. Combined with the fact that training is still deep in the LR warmup phase (~5e-5 LR), the behavior is expected and the run is confirmed to be on a solid trajectory for robust convergence.

The Bucketed Shuffle: How Analytical Optimization Rescued a Distributed Training Run from the Padding Tax 1956 words

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