Near-Duplicate Image Selection Transfers Preferences in Vision–Language Models

Subliminal learning is the phenomenon in which a model transmits its preferences to another model through data that do not explicitly express them. As models increasingly curate training data for other models, we ask whether this transfer can arise from choosing among near-duplicate images while keeping the source photographs and human-written captions fixed. We show that this restricted form of image selection is sufficient. For each photograph, a curator selects one of eight near-duplicate variants by caption likelihood, without target-specific prompts. Students trained on target-preferring curators’ selections show stronger target preferences in an image-free likelihood readout than those trained on neutral selections. We observe owl preference transfer in LLaVA-1.5, Qwen2.5-VL, Qwen3-VL, and Gemma 4, with curators and students initialized from the same base checkpoint. The owl transfer results repeat across independently trained curator ensembles and student seeds on a shared image bank. Target rotations also produce shifts toward camels, oak trees, and a political figure. Experiments on a disjoint source bank provide additional evidence for transfer in LLaVA-1.5 and Qwen3-VL, and in Qwen2.5-VL only with a longer training schedule. Transfer strength depends on dataset size, learning rate, and training budget. First-generation students also pass the owl preference to a second generation in LLaVA-1.5 and Qwen3-VL through captions that do not explicitly mention owls. Under new student seeds, mean preference shifts remain positive in both models, and transfer is observed again in Qwen3-VL. These findings identify near-duplicate image selection as a channel for preference transfer whose strength and onward transmission depend on student training settings.