Robust Reasoning via Dynamic Token Selection for Distribution-Aligned Self-Distillation
Self-distillation improves learning efficiency by rewriting reference answers as training data that better matches the model's own distribution. However, reference answers also introduce strong stylistic biases, causing the generative model to imitate surface forms rather than learn useful reasoning patterns. We observe that the rewriting data contains a large number of high-perplexity (PPL) tokens, coming from two distinct sources: beneficial knowledge-enhancing logical corrections, and harmful stylistic drift induced by reference imitation. Treating all such tokens equally can disrupt the base model's original distribution and degrade performance, especially on difficult reasoning tasks...
| Authors | Ruiqi Zhang, Lingxiang Wang, Hainan Zhang Zhiming Zheng |
|---|---|
| Primary category | cs.CL |
| Published | 30 May 2026 |
| Last updated at source | 30 May 2026 |
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