Agents iterate their own data recipes: math and code selection strategies gain up to 13.34% under fixed training
Agents use experimental feedback to revise data-engineering strategies, and across four tasks—math and code data selection, math reward design, and math curriculum learning—the selected strategies gain up to 13.34% absolute over generic baselines on AIME25 Pass@32 under fixed training conditions.