[P] RL-training Qwen3.6 to RL-train tool using AI models [P]

Short summary

A developer built and open-sourced a recursive RL system where a Qwen3.6-35B agent learns to write complete training jobs for smaller Qwen models, with the inner model's improvement as the reward signal. Over 54 outer-loop steps (~1,750 GPU jobs, ~$1.3k total), the agent learned to pick better base models, use hyperparameters, and generalize to held-out tasks. The full harness, task families, and code are on GitHub.

• •RL-trained Qwen3.6 agent that writes and dispatches RL training jobs for smaller models
• •Achieved 0.0→0.63 reward over 54 steps with generalization to held-out tasks at ~$1.3k total cost
• •Fully open-sourced: harness, reward code, GPU orchestration, and Tinker RL scripts on GitHub

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