AI Trains AI: What a Public Self-Improvement Experiment Actually Closed the Loop On

AI Trains AI：一次公开实验如何校准自我改进的故事

Dan Austin open-sourced a full AI-trains-AI loop. An outer Qwen3.6 agent designs post-training recipes; an inner Qwen3-0.6B or 1.7B model runs real GPU training, and hidden eval scores feed back as reward to update the outer policy. Over 54 steps, the agent first learned to reduce invalid submissions, then shifted 1.7B model usage from 42% to 95% and began tuning temperature, optimizer, and other hyperparameters. Trained small-model scores rose from noise level into the 0.22–0.48 range, with limited transfer to a held-out triage task. A postmortem also revealed an evaluator bug: the old tool-use detector looked for `.function.name` instead of `.name`, so the 0.4-weighted score never fired—yet the reward curve still climbed. The fix required a full restart. The experiment shows outer RL can reshape a training agent's behavior, but tasks, rewards, and budgets are still human-designed.

Why it matters: Dan Austin open-sourced a full AI-trains-AI experiment: an outer Qwen3.6 agent designs post-training recipes, inner models actually train on GPU, and after 54 steps the agent learned to pick models and tune hyperparams, pushing 1.7B usage from 42% to 95%. All three HKR axes hi...