Validity-Calibrated Reasoning Distillation
Reasoning distillation aims to transfer multi-step reasoning capabilities from large language models to smaller, more efficient ones. While recent methods have shown promising gains, they typically rely on static teacher-student hierarchies and frame distillation as trajectory imitation. This is misaligned with the structure of reasoning, where intermediate steps are often locally under-specified: global correctness constrains the final answer, but does not uniquely determine each intermediate move. We propose validity-calibrated reasoning distillation, a framework that treats reasoning distillation as a problem of local learning-signal allocation rather than path alignment. Instead of en...
| Authors | Khouloud Saadi, Di Wang |
|---|---|
| Primary category | cs.LG |
| Published | 14 April 2026 |
| Last updated at source | 9 May 2026 |
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