Using RL to Train Qwen to Train Other AI Models

DanAiTuning · reddit · 2026-07-15

A developer successfully used reinforcement learning to train an agent capable of autonomously training other AI models, and has open-sourced the entire codebase.

Core Mechanism: A trainer agent was built based on Qwen3.6-35B-A3B. It can write complete training tasks (including environment, rewards, datasets, and hyperparameters) and schedule real GPUs for execution. When the internal model scores higher on hidden evaluations, the agent is rewarded, creating an "RL inception" loop.
Key Results: After roughly 54 outer loops (involving about 1750 real GPU training tasks), the reward score climbed from 0 to a peak of 0.63. The skill successfully generalized to unseen new tasks. The agent also learned to prefer the 1.7B base model over the 0.6B one and became more aggressive with hyperparameter configurations.
Cost & Details: The total experiment cost about $1300, with a single internal training run costing just $0.13-$0.30. The learning process occurred in two steps: first learning "not to crash," then learning "to train better models."
Open Source Contents: The GitHub repo includes the complete framework, task families, reward code, GPU orchestration scripts, and a review of failure cases.

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Using RL to Train Qwen to Train Other AI Models

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