Racing Against Humans: MLE-Bench Unleashes AI in Real-World Kaggle Challenges

The MLE-Bench project introduces a comprehensive benchmark designed to evaluate the performance of AI agents in machine learning engineering (MLE) tasks. This benchmark consists of 75 carefully curated competitions sourced from Kaggle, encompassing a variety of real-world challenges that require skills such as model training, dataset preparation, and experimental execution. The competitions span multiple domains, including natural language processing, computer vision, and signal processing, with a total prize pool of nearly $2 million.

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The primary goal of MLE-Bench is to provide a robust framework for assessing the capabilities of AI agents in autonomous MLE tasks, allowing for comparisons against human performance. The authors established human baselines by utilizing Kaggle's publicly available leaderboards, which serve as a reference point for evaluating AI agents. The benchmark's design emphasizes two key aspects: the selection of challenging tasks that reflect contemporary MLE work and the ability to compare AI performance with human-level achievements.

In their evaluations, the authors tested several advanced language models using open-source agent scaffolds. The standout performer was OpenAI's o1-preview model combined with AIDE scaffolding, which achieved a bronze medal level in 16.9% of the competitions. Notably, performance improved significantly when agents were allowed multiple attempts; for instance, the o1-preview's success rate increased to 34.1% with eight attempts. The study also highlighted that while AI agents excelled in competitions solvable by established methods, they struggled with debugging and recovering from errors.

The MLE-Bench framework is designed to be agnostic to the specific methods employed by agents, requiring only a CSV file for submission. Each competition includes a description, dataset, grading code, and a snapshot of the leaderboard for comparison. The authors implemented a grading system that mirrors Kaggle's medal-awarding structure, allowing for a consistent evaluation of agent submissions against human competitors.

To ensure the integrity of the evaluations, the authors established strict rules prohibiting agents from directly writing predictions to submission files or accessing external solutions. They also employed plagiarism detection tools to prevent agents from submitting code that closely resembles existing solutions. These measures aim to maintain a fair competitive environment and accurately assess the capabilities of AI agents.

The experiments were conducted in a controlled environment, with agents given a maximum of 24 hours to complete each competition. The setup included substantial computational resources, allowing for a thorough evaluation of agent performance across the selected tasks.

In conclusion, MLE-Bench represents a significant advancement in the assessment of AI agents' MLE capabilities. By providing a structured and rigorous evaluation framework, it opens avenues for further research into the autonomous capabilities of AI in machine learning engineering, while also highlighting the challenges and limitations that remain in this rapidly evolving field. The open-source nature of the benchmark encourages ongoing exploration and development in understanding AI's role in MLE.

In a comprehensive study evaluating the performance of various AI scaffolds and models in Kaggle competitions, researchers focused on the capabilities of GPT-4o using three distinct open-source scaffolds: AIDE, MLAB, and OpenHands. AIDE, specifically designed for Kaggle competitions, outperformed the other two scaffolds, achieving an average of 8.7% medals compared to MLAB's 0.8% and OpenHands' 4.4%. This suggests that specialized scaffolding can significantly enhance performance in competitive environments.

The study further explored the impact of different underlying models using the AIDE scaffold. Among the four models tested, o1-preview emerged as the most effective, securing medals in 16.9% of competitions—almost double the next best model's performance. Notably, o1-preview's average of 7 gold medals on MLE-bench highlights its superior capability, especially considering that achieving five gold medals qualifies a participant as a Kaggle Grandmaster.

Despite the promising results, the study identified several limitations in the agents' performance. Many agents struggled to create valid submissions, often failing to utilize the validation server effectively. MLAB and OpenHands frequently terminated their runs prematurely, while AIDE's design encouraged continuous optimization until the time limit or maximum submissions were reached. This indicates that the structure and prompts provided by the scaffold can significantly influence the agents' performance.

The researchers also examined how increasing the number of attempts affected performance. Results indicated a consistent increase in medal achievement as attempts increased, demonstrating the value of iterative improvement in competitive settings. Additionally, the study assessed the impact of hardware configurations on performance. Surprisingly, GPT-4o's performance remained consistent across different setups, suggesting that the model did not leverage additional computational resources effectively.

Time constraints were another factor considered in the experiments. By extending the time limit to 100 hours and increasing the maximum nodes allowed, GPT-4o (AIDE) showed a gradual accumulation of medals, although its performance fluctuated over time. This highlights the importance of time for iterative refinement in achieving optimal results.

Contamination and plagiarism were critical concerns addressed in the study. The researchers investigated whether familiarity with competition materials inflated performance metrics. Their findings indicated no significant correlation between GPT-4o's familiarity with competition discussions and its performance, suggesting that the model did not rely on memorized solutions. Furthermore, obfuscating competition descriptions did not adversely affect performance, reinforcing the conclusion that contamination effects were minimal.

In summary, the study underscores the importance of specialized scaffolding, iterative attempts, and time management in enhancing AI performance in competitive environments. While GPT-4o demonstrated strong capabilities, the findings also highlight the need for ongoing evaluation of contamination risks and the limitations of current benchmarks in capturing the full spectrum of AI R&D capabilities.

The MLE-bench benchmark has been developed to evaluate AI agents on machine learning (ML) engineering tasks by utilizing Kaggle competitions. However, it diverges from traditional competitions by employing distinct train-test splits and re-implementing grading codes, which raises questions about the comparability of scores with human leaderboards. To address this, MLE-bench ensures that the new datasets maintain a similar distribution to the originals, confirming that both sample and gold submissions yield results consistent with human performance. Additionally, the benchmark acknowledges that advancements in algorithms may render older competitions easier, prompting the introduction of complexity labels that reflect the current capabilities of ML engineers.

Running MLE-bench is resource-intensive, requiring substantial computational power—approximately 1,800 GPU hours for 75 competitions, with each competition taking 24 hours. The experiments conducted with the o1-preview model and AIDE framework utilized an average of 127.5 million input tokens and 15 million output tokens per seed across the competitions, highlighting the benchmark's demanding nature.

The implications of AI agents capable of conducting autonomous ML research are profound. Such advancements could expedite scientific progress across various fields, enhance safety and alignment research, and stimulate economic growth through innovative product development. However, the rapid evolution of these agents poses risks, as they may outpace human researchers in generating innovations. This could lead to the creation of powerful models that might cause significant harm if not properly managed. The authors suggest that a model proficient in solving a majority of MLE-bench tasks likely possesses the ability to undertake numerous open-ended ML tasks. To mitigate risks, they are open-sourcing MLE-bench to promote research into the capabilities of language models and to enhance transparency regarding acceleration risks in research environments.

The benchmark aims to closely replicate the experience of participating in Kaggle competitions, allowing for direct comparisons between AI agents and human competitors. Initial findings indicate that frontier models, when combined with agent scaffolding, can achieve medals in approximately 16.9% of competitions. By making MLE-bench publicly available, the authors hope to foster further research into the autonomous execution of ML engineering tasks, which is crucial for the safe deployment of advanced models.

Ethically, MLE-bench utilizes publicly available Kaggle competitions and does not involve sensitive data. The authors emphasize the importance of responsible development and alignment with societal norms in AI applications. They also ensure that their setup is reproducible, providing comprehensive details for replicating results, including dataset curation and evaluation metrics. However, they acknowledge that the high compute and token costs may pose challenges for users attempting to fully reproduce the experiments.

In summary, MLE-bench represents a significant step in evaluating AI agents' capabilities in ML engineering, while also addressing the ethical and practical implications of advancing AI technologies. The benchmark's design and findings contribute to a deeper understanding of how AI can autonomously perform complex ML tasks, paving the way for safer and more effective AI deployment in the future.

The document outlines the experimental setup and modifications made to various AI agents participating in a machine learning competition framework called MLE-bench. The agents, specifically AIDE, OpenHands, and MLAgentBench, were evaluated based on their ability to solve machine learning tasks and produce valid submission files.

The experimental environment utilized Microsoft Azure's Standard_NV36ads_A10_v5 virtual machines, equipped with powerful AMD EPYC CPUs and Nvidia A10 GPUs. Each agent operated within Docker containers, ensuring a robust and isolated execution environment. The agents were provided with specific instructions and hyperparameters tailored to their respective frameworks, which included time limits, maximum steps, and model specifications.

Key Modifications and Features of Each Agent:

1. AIDE Modifications:

   • Implemented exponential backoff for API calls to manage high traffic.
   • Fixed the feedback model to use a specific version of GPT-4, focusing on formatting rather than reasoning.
   • Enhanced output format enforcement to prevent invalid feedback responses.
   • Emphasized the importance of generating a valid submission file and tracked whether the submission was created.

2. OpenHands Modifications:

   • Adjusted Docker configurations to enable GPU passthrough and optimize resource allocation.
   • No changes were made to the agent's tooling or behavior, maintaining its original functionality.

3. MLAgentBench Modifications:

   • Introduced a "Validate Submission" tool to check the format of submission files.
   • Added automatic retries for API errors to improve reliability.
   • Clarified the use of the “Final Answer” tool to discourage premature termination of runs.
   • Enhanced error messaging for better debugging.

Dataset and Competition Structure:

The document also details the dataset used for the competitions, which includes a variety of machine learning tasks ranging from image classification to text normalization. Each competition was structured with a specific train/test split, typically maintaining a 10% test ratio. The dataset was derived from publicly available training data, with new test splits created to ensure a fair evaluation of the agents.

Conclusion:

The modifications and structured setup of the agents in the MLE-bench framework highlight the importance of robust design in AI competitions. By focusing on both performance metrics and the validity of submissions, the framework aims to foster a competitive environment that encourages innovation while ensuring compliance with submission standards. The careful orchestration of resources, instructions, and modifications across different agents illustrates a comprehensive approach to evaluating AI capabilities in solving complex machine learning tasks.

The text outlines various datasets used in machine learning competitions, detailing the training and testing splits for each dataset. The datasets cover a range of applications, from medical imaging to molecular predictions, and the proportions of training to testing samples vary significantly across competitions.

For instance, the osic-pulmonary-fibrosis-progression dataset includes data from 176 unique patients for training and approximately 170 for testing, maintaining a roughly 50% ratio. Similarly, the petfinder-pawpularity-score dataset has 9,912 training samples and around 6,800 testing samples, resulting in a 41% testing ratio. The plant-pathology-2021-fgvc8 dataset features 18,632 training samples and 5,000 testing samples, yielding a 34% ratio.

In the seti-breakthrough-listen competition, the training set consists of 60,000 samples, while the test set has 39,995 samples, which is about 40%. The statoil-iceberg-classifier-challenge has a smaller training set of 1,604 samples but a larger test set of approximately 8,424 samples, resulting in an 84% testing ratio. The tensorflow-speech-recognition-challenge presents a significant imbalance with 64,727 training samples and 158,539 testing samples, leading to a 71% ratio.

The tgs-salt-identification-challenge has 4,000 training samples and around 18,000 testing samples, resulting in an 82% ratio. In contrast, the tweet-sentiment-extraction dataset has a much smaller testing ratio of 11%, with 27,481 training samples and 3,534 testing samples. The us-patent-phrase-to-phrase-matching dataset has a 25% testing ratio with 36,473 training samples and approximately 12,000 testing samples.

The uw-madison-gi-tract-image-segmentation dataset is notable for its unique approach, where the test split is created by splitting cases at a 10% ratio, ensuring some cases are entirely in either the test or training set. The ventilator-pressure-prediction dataset features a large training set of around 6 million samples and a test set of approximately 4 million samples, maintaining a 40% ratio.

High complexity competitions, such as 3d-object-detection-for-autonomous-vehicles and bms-molecular-translation, show varied ratios, with the former having an 18% testing ratio and the latter a 40% ratio. The google-research-identify-contrails-reduce-global-warming competition has a notably low testing ratio of 8%, with only 1,856 test samples derived from the original training set.

The champs-scalar-coupling competition focuses on predicting interactions between atoms in molecules, utilizing a dataset that includes various features such as dipole moments and magnetic shielding tensors. The evaluation metric is based on the Log of the Mean Absolute Error, emphasizing the importance of accurate predictions in molecular interactions.

Overall, the datasets illustrate the diversity in machine learning challenges, with varying sample sizes and testing ratios that reflect the complexity and nature of the tasks involved. This variety underscores the need for tailored approaches in model development and evaluation across different domains.

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