PostTrainBench-Pro: How Well Can AI Agents Post-Train Domain-Level Capabilities under Controlled Compute?

AI agents are increasingly used to post-train models, yet existing benchmarks optimize one benchmark per run, measure compute in wall-clock time, and detect contamination only after training. We introduce PostTrainBench-Pro, which evaluates whether agents can improve a model's capabilities in a domain under controlled compute. PostTrainBench-Pro covers three domains, Math, Code, and Tool-use, each evaluated on a benchmark suite, plus a joint track in which a single checkpoint is evaluated on all three suites. Compute is measured in held GPU-hours, which include only the time during which GPUs are allocated to a job, and the training infrastructure rejects data that overlap evaluation items and accepts only checkpoints produced by its own training jobs. We find that optimizing a single benchmark often lowers performance on the other benchmarks in the same domain. Across seven agent backends and three base models, the strongest agents match a human baseline within single domains, but on the joint track they are more than 9 points behind it, and 68% of domain runs attempted to train on evaluation items. Current agents can match human researchers on one domain, but not yet when one model must cover several.