Transluce automates the hunt for training data with surprising side effects, yielding 6,575 datasets that mostly transfer across models
Researchers at Transluce and Berkeley turned datasets with surprising side effects from hand-built curiosities into something searchable at scale: given a target behavior, evolution, reinforcement learning and two subliminal-learning rewrites generate candidate data, and judges never used in the search filter it, producing 6,575 datasets across 8 behaviors. The data reads like ordinary post-training data yet makes a finetuned model show the target behavior; on 64 datasets, ML researchers predicted the side effect 63% of the time against 50% by chance.