Validity-Calibrated Reasoning Distillation
Read the original on arXiv AI →The paper introduces Validity-Calibrated Reasoning Distillation, a new framework for transferring multi-step reasoning from large language models to smaller ones. Unlike prior methods that rely on static teacher‑student hierarchies and trajectory imitation, this approach treats distillation as a local learning‑signal allocation problem. By comparing the teacher’s and student’s next‑step actions under the same prefix and modulating updates based on their relative local validity, the method provides dynamic, context‑dependent supervision that preserves structural guidance while adapting to local reasoning quality. Experiments on math, code, and instruction‑following benchmarks show that this strategy outperforms strong distillation baselines.
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