Woofun AI reports that the open-source model GLM-5.2 has reached the top of the PostTrainBench self-tuning leaderboard. Skeptic scaling01 criticized the result, arguing that the rapid ascent from 22nd to first place is unusual and that the lack of a hidden test set encourages gaming behavior, rendering the model impractical for real-world deployment. Supporters countered that targeted optimization is standard in machine learning, especially under constraints of a single H100 GPU and a 10-hour limit. They noted that public logs show GLM-5.2 follows a clear experimental logic, including autonomous data collection, pipeline planning, and rejection sampling to mitigate overfitting.

Benchmark author Maksym Andriushchenko reviewed the logs and clarified that GLM-5.2 shows fundamental differences in data collection, strategy combination, and decision paths compared to Claude, confirming no imitation or distillation occurred. This transparency effectively debunks industry rumors regarding heavy distillation of Claude by domestic large models, serving as direct evidence of the model's independent research and development capabilities.