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October 18, 2025The Three Faces of Model Training: Closed, Anchored, and Open Loops
Imagine teaching a child only using their own drawings. At first, they might get better at drawing, but soon all their drawings start to look the same. The details fade, and everything becomes blurry. This is what happens when AI models train only on their own output. They might still produce the right answers, but the soul of the data is lost. This is called model collapse, and it is a growing concern as AI systems generate more of the data they learn from.
In the closed loop, a model trains only on its own output. This is like a writer who only reads their own books. At first, the writing seems fine, but over time, the style becomes repetitive and hollow. The same happens to AI. The model might still get the right answer on a test, but it loses the ability to reason in new ways. It becomes a shallow echo of itself. This collapse happens quickly in closed systems.
- Divergence to human baselines over generations
- Grounding rate: how often the model can link its knowledge to real-world facts
- Multi-hop reasoning consistency: the ability to chain ideas without falling into contradictions
Why Fidelity Matters: More Than Just Accuracy
Accuracy measures if an answer is right, but fidelity measures if the answer makes sense in the real world. A model might correctly answer that the sky is blue but fail to explain why it is blue. It might know that exercise is healthy but not understand how it affects the heart. This disconnect is what researchers call the loss of fidelity. It is not just about being correct, but about staying grounded.
The risk is not that AI will stop working, but that it will work too well on surface tasks while losing its grip on deeper meaning. This is why experts recommend open loop systems with frequent human feedback. They act as a grounding wire, much like how a tree needs roots to stay upright during a storm. Without them, the tree might look healthy while slowly dying from the inside out. Communities building AI systems are now creating fidelity benchmarks to catch these issues early. They track divergence from human baselines, the rate of grounding, and the consistency of complex reasoning. These are the vital signs of a healthy AI system, and they are just as important as any accuracy score.
