Learning to Pose Problems: Reasoning-Driven and Solver-Adaptive Data Synthesis
The framework creates high-quality training data for reasoning models by developing a problem generator that reasons about problem structure before synthesis and adapts difficulty based on how well a solver performs. It uses human question traces to learn design rationale, then optimizes the generator using solver feedback to create problems near the solver's current skill level, leading to significant performance gains.