Wenhao Yu

Beautiful Tencent paper. Shows a language model that keeps improving itself using only 1% to 5% human labeled questions while reaching the level of systems trained on about 20 times more data. Earlier self play systems let a model write and solve its own questions, but over time it drifts, repeats narrow patterns, and can even perform worse. Their method runs a challenger copy that generates questions and a solver copy that answers them, turning training into a question answer game between 2 agents. When the challenger writes, it sometimes sees a few real human question answer pairs, which pull its synthetic questions toward realistic tasks instead of strange, off topic puzzles. For each question, the solver tries several answers, the system estimates its success rate, and training keeps mainly mid difficulty questions where the solver is uncertain but not lost. Because both human and synthetic questions pass this filter, the solver trains on focused, non trivial problems, avoids cheap tricks like inflating question length, and gains stronger math and general reasoning scores than earlier self play methods. ---- Paper Link – arxiv. org/abs/2512.02472 Paper Title: "Guided Self-Evolving LLMs with Minimal Human Supervision"