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Visit 5 The Sharpening Tax in Post-Training The Sharpening Tax in Post-Training ↗
Examines how post-training methods sharpen a language model’s output distribution, increasing preference for its highest-probability responses while potentially reducing diversity, calibration, and exploration. The paper analyzes this “sharpening tax” and discusses ways to improve post-training without sacrificing useful model breadth.
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