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.
Why am I seeing this Ranked on source trust — arXiv
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| Factor | Weight | Score | Contribution | Where it came from |
|---|---|---|---|---|
| Corroboration | 0.35 | 0.39 | +0.135 26% | 1 independent org on the story. Tier-3 aggregators never corroborate — they can show something is circulating, never that it is true. |
| Source trustleads | 0.25 | 0.85 | +0.212 41% | arXiv is the highest-trust source on this story and is first-party — the organisation announcing its own news. Trust is taken from the best source, not averaged. |
| Pickup rate | 0.20 | 0.00 | +0.000 0% | One counted organisation, so there is no spread to measure — nothing has picked this up to set a rate. |
| Freshness | 0.20 | 0.85 | +0.171 33% | Halves every 10 hours from the newest item on the story. This is the only factor that rewards a story for nothing more than being recent. |
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What happened
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.
How this story arrived
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- 01 Arxivfirst-party first seen AI Training Manager: Bounded Closed-Loop Control of Adaptive Training Recipes
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