redai-infra/hint-tuning

★ 19PythonAudience · researcherComplexity · 4/5Setup · hard

Mindmap

mindmap
  root((hint-tuning))
    What it does
      Adaptive reasoning training
      Difficulty-based hints
      1K training dataset
    How it works
      Thinking model
      Instruct model
      Minimum hint search
    Categories
      Easy no hints
      Medium small snippet
      Hard full chain
    Outputs
      4B parameter model
      7B parameter model
      Eval benchmarks
    Requirements
      GPU servers
      AI model servers
      Python scripts

mindmap root((hint-tuning)) What it does Adaptive reasoning training Difficulty-based hints 1K training dataset How it works Thinking model Instruct model Minimum hint search Categories Easy no hints Medium small snippet Hard full chain Outputs 4B parameter model 7B parameter model Eval benchmarks Requirements GPU servers AI model servers Python scripts

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Things people build with this

USE CASE 1

Reproduce a training dataset where AI models get only as much reasoning as each math problem actually requires.

USE CASE 2

Download and evaluate a pre-trained 4B or 7B reasoning model without rebuilding the dataset from scratch.

USE CASE 3

Study how to measure problem difficulty by finding the minimum reasoning snippet needed to reach the right answer.

USE CASE 4

Run standard math benchmark evaluations on models trained with adaptive hint-length data.

Tech stack

PythonLLM fine-tuningMath benchmarksGPU compute

Getting it running

Difficulty · hard Time to first run · 1day+

Requires GPU servers and experience running AI model servers locally. Not a plug-and-play tool, aimed at researchers with an ML infrastructure background.

No license information was mentioned in the explanation.

In plain English

This is a research project exploring how to train AI reasoning models more effectively by using fewer, more carefully chosen examples. The core idea is that not every problem requires the same amount of step-by-step reasoning, so the training data should reflect that instead of treating all problems the same way. The method works by running two AI models side by side on a set of math problems. One model is a "thinking" model that writes out long, detailed reasoning chains. The other is a simpler "instruct" model that tries to answer directly. For each problem, the code figures out the shortest snippet from the thinking model's reasoning chain that the instruct model actually needs in order to get the right answer. That minimum snippet is a measure of how hard the problem is, and it determines how much step-by-step reasoning gets included in the final training example. Problems fall into three categories: easy ones the instruct model can solve with no hints, medium ones that need a small reasoning snippet, and hard ones where the full reasoning chain is necessary. The result is a 1,000-example training dataset where easy problems get short answers and hard problems get long ones, rather than padding everything with unnecessarily long reasoning or cutting everything short. The repository includes the raw problem set, the finished 1K training dataset, all the scripts to reproduce the dataset from scratch, and evaluation code that tests the trained models on standard math benchmarks. Two trained models are available for download: a 4-billion-parameter version and a 7-billion-parameter version. This is primarily a research artifact aimed at people working on AI model training, not a general-purpose tool. Running the data construction pipeline requires access to GPU servers and some familiarity with running AI model servers locally.

Copy-paste prompts

Prompt 1

I have the hint-tuning repo from redai-infra. Explain step by step how to run the data construction pipeline to generate the 1,000-example training dataset from the raw problem set.

Prompt 2

Using the hint-tuning approach, how do I set up the two AI models (thinking model and instruct model) locally so I can find the minimum reasoning snippet for each math problem?

Prompt 3

I want to evaluate one of the pre-trained hint-tuning models (4B or 7B) on a standard math benchmark. Walk me through the evaluation scripts in this repo.

Prompt 4

Explain the three problem difficulty categories in hint-tuning (easy, medium, hard) and how the training data is constructed differently for each one.

Prompt 5

I want to adapt the hint-tuning pipeline to a different problem set instead of math. What parts of the scripts would I need to change?

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