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This paper proposes a novel training-free framework to achieve efficient reasoning that reduces token generation costs without sacrificing performance, and investigates the latent representations and proposes a novel training-free framework.
On-Policy Distillation (OPD) has emerged as a widely adopted post-training paradigm for enhancing large language models in reasoning domains. However, the data-centric mechanisms in OPD remain relatively underexplored. This paper presents an empirical study of data efficiency and data selection in OPD. We begin by inve...
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