Training Design for Text-to-Image Models: Lessons from Ablations

This is the second part of a series on training efficient text-to-image models from scratch. It documents training techniques that improved convergence and representation learning for the PRX model, including representation alignment, training objectives, token routing, and data strategies.

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In this post, we shift our focus from architecture to training. The goal is to document what actually moved the needle for us when trying to make models train faster, converge more reliably, and learn better representations.
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