Hint Tuning: Less Data Makes Better Reasoners

• URL: http://arxiv.org/abs/2605.08665v1
• Date: Sat, 09 May 2026 04:07:16 GMT
• Title: Hint Tuning: Less Data Makes Better Reasoners
• Abstract summary: Hint Tuning is a data-efficient approach that teaches models to calibrate reasoning depth.<n>By testing what the instruct model can solve with varying guidance, we automatically construct training data across three states.<n>We achieve superior efficiency through simple alignment with the instruct model's capabilities.
• Score: 30.274192211828723
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large reasoning models achieve high accuracy through extended chain-of-thought but generate 5--8 more tokens than necessary, applying verbose reasoning uniformly regardless of problem difficulty. We propose Hint Tuning, a data-efficient approach that teaches models to calibrate reasoning depth. Our key insight: the corresponding instruct model serves as an ideal difficulty probe. By testing what the instruct model can solve with varying guidance, we automatically construct training data across three states: No-Hint (direct answer), Sparse-Hint (minimal prefix), and Full-Hint (complete reasoning). This converts the abstract challenge of difficulty labeling into a measurable consistency check between the instruct and reasoning models. With only 1K self-annotated samples, Hint Tuning achieves 24--66% token reduction (31.5% average) across mainstream reasoning models (Qwen3-Thinking, DeepSeek-R1-Distill) at multiple scales (4B--32B) while maintaining competitive accuracy on five benchmarks. Unlike methods requiring massive distillation datasets or expensive RL, we achieve superior efficiency through simple alignment with the instruct model's capabilities.

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