Hint Tuning 1K: Supervised Fine-Tuning Data for Chain-of-Thought Reasoning

redai-infra released the Hint Tuning 1K dataset on Hugging Face in June 2026. It is a resource for constructing supervised fine-tuning data using a method that generates long and short chain-of-thought reasoning traces. The method uses an instruct model as a difficulty probe to determine the appropriate reasoning hint length for each problem.

Use Cases

• Fine-tuning language models for improved reasoning based on the described hint-tuning methodology.
• Studying the relationship between problem difficulty and required reasoning hint length as described in the construction method.
• Benchmarking model performance on problems with calibrated chain-of-thought annotations.
• Developing new data construction techniques for supervised fine-tuning inspired by the ideal difficulty probe concept.

Strengths

• Dataset is associated with official code for the described 'Hint Tuning' method.
• Data construction method is explicitly defined, using an instruct model to probe problem difficulty.
• Last updated on 2026-06-03, indicating recent maintenance.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count, file formats, and license information are unknown, which may limit suitability assessment.

Provenance

Source

redai-infra on Hugging Face

Collection Method

Constructed using the Hint Tuning method, which uses an instruct model as an ideal difficulty probe to generate chain-of-thought traces.

Freshness

Last updated 2026-06-03 11:21:54; freshness should be verified.