AI Training Manager: Bounded Closed-Loop Control of Adaptive Training Recipes
We present the AI Training Manager, a bounded LLM-based metacognitive monitoring-and-control layer for machine-learning training. The Manager asynchronously observes structured telemetry from an active training run, assesses the current training regime, and selects adaptive interventions through a constrained, deterministically verified action interface. We evaluate the feasibility of this architecture using deliberately induced, interpretable failure modes in supervised and reinforcement learning tasks. For supervised learning, we induce a multi-objective overfitting failure in a GPT-2-style model trained on TinyStories. The Manager prevents the resulting late-run validation collapse, re...
| Authors | Anjali Rao, Nikhil Kamalkumar Advani |
|---|---|
| Primary category | cs.AI |
| Published | 29 June 2026 |
| Last updated at source | 30 September 2026 |
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