The Model Organism Lottery: Model Organism Interpretability Strongly Depends on Training Methodology

Model organisms—AI models trained to exhibit quirks for testing interpretability—might give researchers false confidence in safety auditing, per LessWrong research by Gabriel KS. In a study spanning fifty-four models trained seven different ways, interpretability results varied significantly based on training approach, even when all models showed the same behavior. This matters because current benchmarks rely on a single training method, potentially making these auditing techniques look more ready for real use than they actually are. The researchers recommend benchmarks that include models trained diversely, and caution against treating any single result as individually meaningful.

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