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Contextual Early Reward (CER), which predicts terminal reward through behavioral evidence in a trajectory prefix, provides an interpretable, efficient, and dense evaluation method for long-horizon coding agents.
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.
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