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This paper conducts a systematic study of unsupervised visual pretraining paradigms that directly leverage visual documents without text extraction, showing that Visual Pretraining is a scalable learner for foundation model intelligence.
Experiments on challenging reasoning benchmarks show that H$^2$SD achieves the strongest overall performance among representative RLVR and self-distillation baselines, with stable optimization and a favorable accuracy-efficiency trade-off.
Evaluations across scientific, multimodal, agentic, and general-purpose benchmarks show that Intern-S2-Preview-397B achieves competitive or leading results in multiple settings.
This work presents Memory Decoder at Scale, scaling memory models up to 6.9B parameters and pretraining them on 300B tokens, demonstrating that independently scaling pretrained memory offers a more parameter efficient path to improving language model performance.
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