Training agents on tasks calibrated to be appropriately difficult (not too easy, not impossible) using multiple solver feedback produces better generalization than manually authored or single-solver validated tasks.
CalibForge automatically creates training tasks for AI agents by using multiple solvers to identify tasks that are challenging but solvable—the 'learnable zone.' It revises candidate tasks based on solver disagreement and performance patterns, then trains agents on these calibrated tasks, achieving significant improvements on code and repository understanding benchmarks.