Learning from feedback advances the capabilities and safety of frontier models, but preference supervision remains costly and difficult to scale. We introduce next-token preference (NTP), an unsupervised reward-modeling method that constructs preference pairs from the continuation structure of raw web text. NTP uses each prefix's original continuation as the chosen response and nearby continuations as in-batch negatives, without candidate generation or human or model-generated preference annotations. Training on 11M tokens of math-focused web data yields steady gains on RewardBench and RewardBench 2, with gains across model families and sizes. Across mathematical and general-domain web corpora, our method improves overall RewardBench 2 accuracy by 7.6-8.3 percentage points over initialization under the same token budget. Training on math-focused text yields gains of up to +16.1 on in-domain math subsets and consistent improvements on out-of-domain safety and general subsets. These reward models improve best-of-N selection on math, code, safety, and instruction following, and support math policy optimization competitive with supervised reward models of similar size. NTP also improves the label efficiency of subsequent supervised fine-tuning. Using half the labeled pairs from a curated preference dataset, NTP initialization surpasses base initialization trained on the full dataset. These results suggest that web text contains transferable preference signals that can reduce dependence on costly preference annotations.

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