arXiv:2605.08665v2 Announce Type: replace 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-Hin
The paper addresses a critical current challenge in large language models: balancing robust reasoning with computational efficiency and data requirements.
Improving the efficiency of reasoning models by 'Hint Tuning' can significantly reduce operational costs and data dependency, making AI more accessible and scalable.
Models will become more adept at calibrating their reasoning depth, potentially leading to more targeted and efficient AI applications without sacrificing performance.
- · AI developers
- · Cloud providers
- · Companies using large language models
- · Developers of less efficient reasoning optimization methods
More efficient and cost-effective deployment of advanced reasoning AI models.
Democratization of advanced AI capabilities due to lower computational and data burden.
Accelerated development of complex AI agents that can adapt their reasoning process to task difficulty.
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Read at arXiv cs.CL