An AI that trains on its own simulated practice drifts off course, so how little real-world data can steer it back?

This explores how much real-environment data a learned simulator needs to stay honest, and the corpus doesn't give a number. The closest note is on training AutoResearch agents. It says only that a 'small' real-execution stream is enough, with no percentage, sample count or ratio (Does simulator bias kill world model training for agents?). So a precise threshold isn't something this library can answer.

What that note does say points to a different question. A world model trained and used on its own output has a permanent error floor: its biases never wash out, however long you run it. Anchoring it with a trickle of real execution, using debiasing and denoising, removes that floor. The whole setup cuts training compute three to fourfold. The stream has to be small enough to keep most of that saving, so it works as a correction signal rather than a second training set. The word 'small' here comes from the cost trade-off, not from a measured minimum.

The reason a little real data can go a long way shows up in a neighbouring note on self-improvement. Models that improve on their own output stall because they can't check themselves, and the methods that reliably work all bring in an external anchor: past model versions, third-party judges, user corrections or tool feedback (Can models reliably improve themselves without external feedback?). Read that way, the anchoring stream isn't there to teach the world model the environment. Its job is to reveal which way the simulator is systematically wrong, and that can take far less data than learning the environment from scratch. That's an inference from the two notes, not something either one tests. The ReAct paper gives a related hint at the reasoning level. Interleaving a language model's thinking with real tool feedback beat pure chain-of-thought by 10 to 34% absolute accuracy (Can interleaving reasoning with real-world feedback prevent hallucination?). But that's about grounding a reasoner, not calibrating a simulator, so it shows that occasional contact with reality pays off without saying how occasional it can be.

The library also suggests why the answer is hard to pin down. A model can score well on prediction with task-specific shortcuts and still not have a coherent picture of how the environment works (What makes a world model actually useful for reasoning?). Bias may therefore be invisible until the model is pushed into situations its shortcuts don't cover. LLM world models are also built from text written by people who had contact with reality, and that chain has gaps that limit real-time checking and updating (Can large language models develop genuine world models without direct environmental contact?). If the bias hides in rarely visited states, the anchoring stream has to reach those states, and how much data that takes depends on what the stream covers as much as on how big it is.

The corpus doesn't say how small the stream can be. It says a small stream is enough to remove the bias floor, and it suggests that what the stream measures matters more than its volume.