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This research introduces the AI Training Manager, a bounded LLM layer that autonomously monitors training telemetry and executes verified interventions to prevent overfitting and collapse in both supervised and reinforcement learning tasks. By acting as a metacognitive controller, the system significantly improves validation loss reduction and safe success rates under stress regimes without requiring model retraining. The findings demonstrate that constrained LLM reasoning can effectively serve as a practical safety and optimization mechanism for ongoing machine learning processes.
AI Training Manager: Bounded Closed-Loop Control of Adaptive Training Recipes
arXiv:2606.29871v2 Announce Type: replace Abstract: 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 approach on 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, reducing final validation loss by 54.3% relative to the same stressed recipe. For reinforcement learning, we study robotic reaching under opposing multifactor stress regimes. In a conservative regime, the manager raises final deterministic safe success from 0.413 to 0.705. In an aggressive regime, it raises safe success from 0.121 to 0.764. The same manager instructions and action surface are used in both regimes. These results suggest that bounded LLM reasoning can provide a practical metacognitive control layer over ongoing learning processes.
Abstract: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 approach on 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, reducing final validation loss by 54.3% relative to the same stressed recipe. For reinforcement learning, we study robotic reaching under opposing multifactor stress regimes. In a conservative regime, the manager raises final deterministic safe success from 0.413 to 0.705. In an aggressive regime, it raises safe success from 0.121 to 0.764. The same manager instructions and action surface are used in both regimes. These results suggest that bounded LLM reasoning can provide a practical metacognitive control layer over ongoing learning processes.
Artificial Intelligence (cs.AI)
arXiv:2606.29871 [cs.AI]
(or arXiv:2606.29871v2 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.29871 Focus to learn more arXiv-issued DOI via DataCite
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