🤖 AI Summary
This work addresses the limitations of conventional training pipelines, which struggle to dynamically mitigate issues such as overfitting, loss imbalance, and unsafe exploration due to reliance on fixed policies or single-axis schedulers. The authors propose a large language model–based bounded supervisory controller that leverages structured telemetry snapshots to monitor training dynamics in real time and generates verifiable multi-parameter adjustment commands within a constrained action space. This enables closed-loop regulation of learning rate, regularization strength, loss weighting, and exploration strategy. Notably, it introduces pattern-constrained large language models into training supervision for the first time, supporting asynchronous, auditable multi-axis interventions applicable to both supervised and reinforcement learning. Experiments demonstrate a 60% loss reduction on TinyStories with effective overfitting correction, marked alleviation of overly conservative or unsafe exploration in robotic manipulation tasks, and generation of traceable intervention logs.
📝 Abstract
We present the AI Training Manager, a bounded LLM-based supervisory controller for adaptive machine learning training. Standard training pipelines often rely on fixed recipes or single-axis schedulers, which can struggle with mid-run failures such as severe overfitting, loss imbalance, exploration collapse, or unsafe exploration. Rather than replacing mathematical optimizers or acting as an unconstrained coding agent, the manager operates through a schema-conditioned interface: it reads structured telemetry snapshots from an active run, audits a constrained action space, and returns validated updates to training parameters such as learning rate, regularization strength, loss-weight coefficients, and exploration settings. We evaluate this architecture across supervised language modeling and reinforcement learning. On TinyStories, the manager detects and corrects overfitting, achieving a validation loss 60% lower than the baseline while producing auditable intervention logs. In this supervised setting, we additionally show that manager inference does not need to block the training loop: training can continue while a manager response is pending, and validated updates can be applied asynchronously once available. In a robotic manipulation reinforcement-learning task, we use the same bounded decision interface in an episodic closed-loop setting, where manager updates are applied at evaluation or checkpoint boundaries. The manager mitigates both conservative and unsafe exploration regimes. These results suggest that schema-conditioned LLMs can serve as bounded supervisory managers for live training runs, complementing conventional optimizers and schedulers with interpretable, multi-axis intervention capabilities