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Autonomous coding agents can slash inference costs by 33% and actually *improve* task success on SWE-Bench, proving that dynamic, syntax-aware context pruning enhances long-horizon reasoning rather than compromising it.
Transforming pre-training data with reasoning annotations boosts model performance by over 2 percentage points without altering the training objective.
OPD transfers reasoning skills rather than answers, revealing a complex interplay between teacher-student origins that can either enhance or hinder model capabilities.
