Mixture of Training: Recombining Small-Scale Scaffolded Pretraining Runs into a Larger Language Model

We ask whether language-model pre-training can be decomposed into smaller, independently trainable jobs that can later be recomposed into a coherent larger model. We introduce Mixture of Training (MoT), a scaffolded modular pre-training procedure that partitions a target Transformer into contiguous layer blocks, trains each block inside a frozen pretrained aligner scaffold, and then recomposes the trained blocks with an optional short end-to-end adaptation pass. On a 1.3B-parameter Gemma-style m

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ethics.ai (13 August 2026), “Mixture of Training: Recombining Small-Scale Scaffolded Pretraining Runs into a Larger Language Model,” evidence record 19171, https://ethics.ai/record/19171 (originally published by arXiv).

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