The editor of Downcodes will take you to understand the latest research results of OpenAI: MLE-bench benchmark test! This research aims to evaluate the actual capabilities of AI agents in the field of machine learning engineering. The research team selected 75 Kaggle machine learning competitions as test scenarios, covering many aspects such as model training, data preparation, and experimental running, and used Kaggle public ranking data as the human benchmark for comparison. By testing a variety of cutting-edge language models, the research team gained valuable experience and open sourced the benchmark code to facilitate subsequent research.
In a recent study, the OpenAI research team launched a new benchmark called MLE-bench, which is designed to evaluate the performance of AI agents in machine learning engineering.
This study specifically focuses on 75 machine learning engineering-related competitions from Kaggle, which are designed to test a variety of skills required by agents in the real world, including model training, data set preparation, and experiment running.
For better evaluation, the research team used basic data from Kaggle's public rankings to establish human benchmarks for each competition. In the experiment, they used the open source agent architecture to test several cutting-edge language models. The results show that the best performing configuration - OpenAI's o1-preview combined with the AIDE architecture - achieved Kaggle bronze medal levels in 16.9% of the competitions.
Not only that, the research team also conducted in-depth discussions on the resource expansion form of AI agents and studied the contaminating impact of pre-training on the results. They emphasized that these research results provide a basis for further understanding of the capabilities of AI agents in machine learning engineering in the future. To facilitate future research, the team has also made the benchmark code open source for other researchers to use.
The launch of this research marks an important progress in the field of machine learning, especially in how to evaluate and improve the engineering capabilities of AI agents. Scientists hope that MLE-bench can provide more scientific evaluation standards and practical basis for the development of AI technology.
Project entrance: https://openai.com/index/mle-bench/
Highlight:
MLE-bench is a new benchmark designed to evaluate the machine learning engineering capabilities of AI agents.
The research covers 75 Kaggle competitions, testing the agent's model training and data processing capabilities.
? OpenAI’s o1-preview and AIDE architecture combination reached Kaggle bronze level in 16.9% of competitions.
The open source of the MLE-bench benchmark provides a new standard for the evaluation of AI agents in the field of machine learning engineering, and also contributes to the development of AI technology. The editor of Downcodes looks forward to more research results based on MLE-bench in the future!