ATESD introduces a Beta-policy controller that adapts teacher exposure ratio during LLM self-distillation training and reports gains over fixed-exposure baselines on math benchmarks.
Simple statistical gradient-following algorithms for connectionist reinforce- ment learning.Machine Learning, 8(3–4):229–256
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Adaptive Teacher Exposure for Self-Distillation in LLM Reasoning
ATESD introduces a Beta-policy controller that adapts teacher exposure ratio during LLM self-distillation training and reports gains over fixed-exposure baselines on math benchmarks.