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Effective Teacher Support for Reasoning Distillation

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 13/11/2026, 07:48:25 UTC.

Briefings

A new study published on arXiv in November 2026 reports that distilling large language models like Qwen3 and Nemotron from a tiny fraction of their vocabulary (0.01%) is as effective as using the full vocabulary, significantly reducing training costs. The research confirms this approach works across different model sizes and answer-only supervision settings, with no observed performance drop on MMLU-Pro benchmarks.

Key points

• Qwen3 4B models distilled from 100% vocabulary to 0.01% vocabulary retain full MMLU-Pro performance while reducing training step time by 38%.
• Nemotron Nano 12B models match full-vocabulary performance on MMLU-Pro at 0.1% vocabulary retention, unlike smaller models that fail to emit reasoning tags at lower support levels.

Context

This research addresses the high computational cost of standard language model distillation by proposing a support-reduction technique that maintains accuracy while cutting resource usage.

Limitations

The study covers only Qwen3 and Nemotron families; results on other model families remain unverified. The 0.01% support cutoff is a specific finding, not a general rule for all models. Caching and memory optimizations are described as effective but not proven to prevent performance degradation.

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