Subliminal Reinforcement Learning: traits travel through a judge's scores

The paper introduces "Subliminal Reinforcement Learning," a method where a model’s traits are transferred through data that contains no explicit semantic cues. Using a language‑model judge that scores the model’s own outputs, the authors demonstrate that a student model can acquire a preference for a particular animal simply by learning from the judge’s scores, even when the student’s outputs are nonsensical syllables or numbers. Across 48 experimental conditions, 17 met a 5% false‑discovery threshold, while removing the reward difference between judges eliminated the transfer effect, establishing a four‑stage chain that explains how traits propagate. "whyItMatters":"The findings suggest that subtle, non‑semantic signals from evaluators can shape AI behavior, highlighting a potential risk pathway for undesirable dispositions in safety‑critical systems."

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

• reinforcement-learning
• safety