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This study introduces section-aware compression for reasoning traces, training models to drop filler narration while preserving compute and verification spans, matching or exceeding original accuracy while using 2-3 times fewer tokens.
This paper proposes Mixed-Policy Distillation (MPD), a framework that transfers concise reasoning behaviors from large teacher models to smaller student models, reducing token usage by up to 27.1% while improving performance.