From Foundational Competency to Expert Performance: A Curriculum Approach to Model Development

A student does not go from 1st to 12th grade in a single step. Each grade builds on a specific set of skills, and each one assumes the previous skills have already been mastered. Nobody learns calculus without algebra. When a student skips ahead anyway, what they end up with is memorization rather than understanding. The gaps show up later, usually in ways that are harder to trace back and fix. Model training works the same way. Hard tasks are important, but their impact is limited when a model has not yet built the capabilities those tasks assume. A curriculum, meaning a deliberately ordered progression of tasks calibrated to what a model can currently do, gives research teams a way to locate where a model actually stands, identify the specific capabilities it is missing, and target the next round of data at exactly those gaps. Curriculum learning is an established approach in the field. The idea that ordering training tasks from easier to harder can improve both convergence and final performance goes back to the early deep learning research literature: a learner extracts more signal from an example when it already has the prerequisites to interpret it. For agentic coding…