# HardML: A Benchmark For Evaluating Data Science And Machine Learning knowledge and reasoning in AI

arXiv: https://arxiv.org/abs/2501.15627 · HTML: https://mlsanity.com/paper/2501.15627/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Mathematical reasoning, one of 17 directions in LLM reasoning.
- Builds on 15 of its 25 references that are in the map.
- First posted on arXiv 26 Jan 2025.

## Placed in

- LLM reasoning / [Mathematical reasoning](https://mlsanity.com/lr/mathematical-reasoning/)

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Measuring Mathematical Problem Solving With the MATH Dataset](https://mlsanity.com/paper/2103.03874/) (2021)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts](https://mlsanity.com/paper/2310.02255/) (2023)
- [ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission](https://mlsanity.com/paper/1904.05342/) (2019)
- [Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus](https://mlsanity.com/paper/2104.08758/) (2021)
- [MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering](https://mlsanity.com/paper/2410.07095/) (2024)
- [Analysing Mathematical Reasoning Abilities of Neural Models](https://mlsanity.com/paper/1904.01557/) (2019)
- [FrontierMath: A Benchmark for Evaluating Advanced Mathematical Reasoning in AI](https://mlsanity.com/paper/2411.04872/) (2024)
- [Understanding and Benchmarking Artificial Intelligence: OpenAI's o3 Is Not AGI](https://mlsanity.com/paper/2501.07458/) (2025)
- [From Human Days to Machine Seconds: Automatically Answering and Generating Machine Learning Final Exams](https://mlsanity.com/paper/2206.05442/) (2022)

## Built on by (most cited first, 5 of 5)

- [Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models](https://mlsanity.com/paper/2503.09567/) (2025)
- [Reinforcement Learning for Machine Learning Engineering Agents](https://mlsanity.com/paper/2509.01684/) (2025)
- [Measuring Data Science Automation: A Survey of Evaluation Tools for AI Assistants and Agents](https://mlsanity.com/paper/2506.08800/) (2025)
- [Make Still Further Progress: Chain of Thoughts for Tabular Data Leaderboard](https://mlsanity.com/paper/2505.13421/) (2025)
- [AgentDS Technical Report: Benchmarking the Future of Human-AI Collaboration in Domain-Specific Data Science](https://mlsanity.com/paper/2603.19005/) (2026)

## Abstract (arXiv)

We present HardML, a benchmark designed to evaluate the knowledge and reasoning abilities in the fields of data science and machine learning. HardML comprises a diverse set of 100 challenging multiple-choice questions, handcrafted over a period of 6 months, covering the most popular and modern branches of data science and machine learning. These questions are challenging even for a typical Senior Machine Learning Engineer to answer correctly. To minimize the risk of data contamination, HardML uses mostly original content devised by the author. Current state of the art AI models achieve a 30% error rate on this benchmark, which is about 3 times larger than the one achieved on the equivalent, well known MMLU ML. While HardML is limited in scope and not aiming to push the frontier, primarily due to its multiple choice nature, it serves as a rigorous and modern testbed to quantify and track the progress of top AI. While plenty benchmarks and experimentation in LLM evaluation exist in other STEM fields like mathematics, physics and chemistry, the subfields of data science and machine learning remain fairly underexplored.