A researcher used reinforcement learning to train Qwen3.6-35B, a large language model, to automatically design and submit training jobs for smaller models running on GPU clusters.
The trainer agent learns by receiving reward signals when the models it trains improve on hidden benchmarks.
This creates a recursive structure—an RL loop training other RL loops—and achieved a peak reward of approximately 0.63, demonstrating that AI models can learn to optimize training workflows.
What happened
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.
Why it matters
This demonstrates a novel approach to automating machine learning workflow optimization—rather than manual tuning of training configurations, an AI model learns to design training jobs that improve other models' performance. For ML practitioners, this suggests potential efficiency gains in hyperparameter search and training pipeline design, though the work is an early-stage proof of concept.
What to watch
The agent was trained on 6 task families, with one held out entirely as a generalization test. Episode reward climbed from approximately 0.0 to approximately 0.63 at peak performance, indicating the trainer agent learned to write increasingly effective training jobs.
Ask the AI about this article →
This work sits at the intersection of two trends in machine learning: the use of large language models to generate code and configurations, and reinforcement learning applied to meta-learning problems. Rather than treat hyperparameter tuning as a separate search problem, the practitioner embedded it as a task an RL agent learns to solve. The nested structure—RL training an agent that writes RL training jobs—creates a feedback loop where success in the inner loop (the small model improving) directly reinforces the outer agent's decisions. The choice to hold out one task family entirely as a generalization probe is methodologically sound, testing whether the trainer learned general principles rather than task-specific patterns. The reward climbing from approximately 0.0 to approximately 0.63 suggests the agent learned meaningful behavior, though the article does not specify what downstream metrics (accuracy, loss, or other domain-specific measures) the hidden evaluation measured.
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