Researcher RL-trains model to RL-train other models

A machine learning practitioner trained Qwen3.6-35B-A3B, a large language model, using reinforcement learning to act as a trainer agent that writes and submits complete training jobs for smaller models (0.6B or 1.7B Qwen instances) to GPU clusters. The agent receives reward when the models it trains score higher on hidden evaluations, creating a nested loop of RL training inside RL training.