Hint Tuning: Less Data Makes Better Reasoners
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 che...
| Authors | Siqi Fan, Minghao Li, Xiaoqian Ma et al. |
|---|---|
| Primary category | cs.CL |
| Published | 9 May 2026 |
| Last updated at source | 3 June 2026 |
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