GLM 5.2 登顶 PostTrainBench，AI 自主训练弱模型

GLM 5.2 just took the top spot on PostTrainBench by scoring 34.29%.

PostTrainBench tests whether an AI agent can take a raw LLM and make it better by actually training it, not by answering the benchmark questions itself.

The agent gets 4 small base models, 1 H100 GPU, and 10h, then it must choose training data, write training code, run experiments, fix broken runs, and submit improved versions of those models.

So in this case, GLM 5.2 was the agent model controlling the training process, so PostTrainBench did not score GLM 5.2’s own answers; it scored whether GLM 5.2 could take 4 weaker LLMs and improve them within 10h on 1 H100.

The gap to official instruct models, which score 51.14%, still shows how far agents are from mature post-training pipelines built with more data, compute, and human tuning.

GLM 5.2’s job was to write training code, pick or make training data, run fine-tuning, fix failed runs, and submit the newly trained models for testing.