What Does GDP.pdf Teach — Beyond PDFs?

Surge AI post-trained Kimi K2.7 on 1,465 professional-document tasks from its GDP.pdf dataset and reported substantial improvements both on held-out PDF tasks and on GDPval, a separate benchmark requiring workspace exploration, tool use, calculations, and finished business deliverables. Strict success on GDP.pdf increased from 11.0% to 24.5%, while GDPval tasks scoring at least 90% rose from 19.5% to 32.7% and perfect scores from 2.7% to 5.9%. The reported gains extended to tasks without PDFs, suggesting that training improved a broader habit of identifying all relevant evidence before acting rather than merely improving document reading. Comparisons of model trajectories found that the trained model accessed more relevant sources while reading less total text, repeated itself less, and more consistently accounted for complete records, dependencies, and task requirements. Examples included combining all weekly timekeeping sheets before preparing monthly reports, carrying every checklist item into an action tracker, and updating schedules when delays affected completion dates. The authors conclude that realistic, complex PDF tasks can strengthen transferable professional-reasoning skills, although the model already possessed the tools and file-handling abilities needed for the evaluation environment.

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