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This paper proposes environment evolution, which incrementally increases environment difficulty off-policy and schedules the evolved environments generation by generation during training to provide continuous learning signals.
Deep Research Pretraining (DRP), an offline framework that derives predictive navigation supervision from naturally occurring evidence structures, is introduced, an offline framework that derives predictive navigation supervision from naturally occurring evidence structures.
PolicyLong is proposed, shifting data construction towards a dynamic on-policy paradigm, by iteratively re-executing data screening (entropy computation, retrieval, and verification) using the current model, which ensures the training distribution tracks evolving capabilities, yielding an emergent self-curriculum.
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