Near-Duplicate 图片选择可向视觉语言模型传递隐含偏好
Near-Duplicate Image Selection Transfers Preferences in Vision-Language Models
论文挺有意思：策展模型只是挑了挑几乎一样的图片，就把偏好悄悄传给了学生模型，LLaVA、Qwen 这些都中招，做数据清洗的值得看看。
arXiv 论文研究了亚阈学习现象：策展模型仅在八张近乎重复的图片变体中按 caption 似然做选择，源照片和人工 caption 保持不变。在 LLaVA-1.5、Qwen2.5-VL、Qwen3-VL 和 Gemma 4 四个模型上，用偏向目标的策展数据训练的学生模型，在无图像读出中对 owl 目标显示出更高的对数似然比。Qwen3-VL 中贪心选择使学生向目标偏移 10.4 个百分点，其他模型为 0.5-1.3 个百分点。偏好还能通过 caption 传递到第二代学生模型，线性探针区分选择组的准确率最高达 59.7%（随机 50%）。
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. 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 higher mean target-versus-alternatives log-likelihood ratios in an image-free readout than students trained on neutral selections. Under a prespecified decision rule, this directional shift appears for an owl target in LLaVA-1.5, Qwen2.5-VL, Qwen3-VL, and Gemma 4, with curators and students initialized from the same base checkpoint, and target rotations move it toward camels, oak trees, and a political figure. The owl target-selected student also scores above all count-matched random-reselection students in three models and above 55 of 60 in LLaVA-1.5, and Qwen3-VL shows the shift again on a new source bank with newly trained curators. Its size depends on dataset size, learning rate, and training budget; greedy choices among the eight options move toward the target by 10.4 percentage points in Qwen3-VL and 0.5-1.3 points elsewhere. First-generation students also pass the owl preference to a second generation through captions without explicit owl mentions in LLaVA-1.5 and Qwen3-VL. Thus even a deduplication-style choice among near-identical copies can shift students' target-relative readouts with source photographs and captions fixed; the tested linear probes classify the selecting arm with accuracies up to 59.7% (chance 50%).