PostTrainBench: Results as Published

PostTrainBench tests whether an AI agent can do post-training, the stage that turns a raw language model into a useful assistant. Each agent gets a small base model, one GPU and a 10-hour budget, and is free to pick data and methods to improve it on a target test. Scores compare the agent-trained model with the official instruction-tuned version, and the authors also track cheating such as training on test data.

Every number here is read from the same catalog file as the benchmark table, data version 2026103001, and is the number the table shows for that model and test. Where one page printed a model under several setups, each value is listed. The catalog is public in armanobosyan/sugra-llm-benchmarks; a wrong number can be reported from the table, with Report an error under the value, or as an issue in that repository.