In March 2026, a team using NVIDIA’s large-scale language models achieved a significant milestone in automated machine learning: generating more than 600,000 lines of code. This work led to 850 experiments and ultimately won first place in the Kaggle Playground competition, demonstrating the compression of iterative cycles that is essential to the success of modern machine learning. Experiments previously limited by coding and execution speed are now freed up by LLM agents and GPU acceleration. This approach allowed one researcher to “accelerate the discovery of the best-performing tabular data prediction solutions,” ultimately arriving at a superior solution consisting of a complex four-level stack of 150 models.
LLM agent accelerates code generation for Kaggle competitions
In March 2026, over 600,000 lines of code were automatically generated by the Large Language Model (LLM) agent. This marks a shift that will significantly increase automation in machine learning project development and allow complex models to be built quickly. This proliferation of automated code creation is not just a matter of quantity. We are fundamentally changing the pace of experimentation in competitive machine learning environments like Kaggle. Traditionally, there have been significant hurdles in coding and execution speed, but advances in GPU processing and the integration of LLM agents are rapidly eliminating these limitations. This was a case of actively driving iterative improvement rather than just generating functional code. The winning solution was particularly complex, consisting of a “four-level stack of 150 models” and revealed a new standard for ensemble methods and the potential for highly sophisticated model architectures.
This workflow guides the LLM agent through established machine learning best practices, starting with exploratory data analysis (EDA), baseline model creation, and feature engineering, and ending with model combination with hill climbing and stacking. This process leveraged multiple LLM agents, GPT-5.4 Pro, Gemini 3.1 Pro, and Claude Opus 4.6 in a human-based system. The LLM agent was asked to perform tasks such as EDA with instructions such as “Create EDA code to explore the CSV files train.csv and test.csv. Run the code to share the plots and text.” Importantly, the system prioritized rapid iteration and leveraged GPUs and libraries such as NVIDIA cuDF, cuML, XGBoost, and PyTorch to speed up experiment execution.
As the case study authors explain, the LLM agent is “good at both” feature engineering and model tuning, enabling continuous improvement cycles where “each experiment, for better or worse, always saves OOF and test predictions to disk.” This approach allows researchers to explore a wider range of ideas and quickly refine models, ultimately leading to more performant solutions for tabular data prediction tasks.
GPT-5.4, Gemini, and Claude drive tabular data experimentation
The pace of machine learning model development is changing significantly with the integration of large-scale language models (LLMs) into the experimentation process. This represents a significant acceleration of the iterative cycle of model building, a process traditionally constrained by coding and execution speed. Success is not just about the amount of automation. It reflects a fundamental change in the way experiments are conducted. The winning Kaggle solution was designed to predict telecom customer churn and was not a single model, but a complex four-level stack of 150 separate models. This complex ensemble highlights the scale of complexity currently achievable with agent assistance and suggests new benchmarks for competitive machine learning. Following EDA, the agent built a baseline model and iteratively refined the model through feature engineering and model tuning. “LLM agents excel at both of these tasks,” the authors say, highlighting their ability to rapidly generate and test new ideas.
To maximize efficiency, experiments were run consistently on GPUs utilizing libraries such as NVIDIA cuDF, NVIDIA cuML, XGBoost, and PyTorch. The workflow culminated in model combination using techniques such as hill climbing and stacking. One of the prompts used to refine the final solution was: “Could you please try combining all the OOF and test PREDs using different meta models? Try hill climbing, ridge/logistic regression, NN, and GBDT stacker. Thanks in advance.” We conclude that the combined power of acceleration provides a path to faster results.
To understand the CSV files train.csv and test.csv, write and run the EDA code. ” Step 2: LLM Agent Builds a Baseline Once LLM understands the data, specifically the feature and target columns, it creates the first complete pipeline for training the kfold model by requesting a specific model from LLM.
Baseline model and feature engineering with LLM guidance
This success marks a shift from simply automating code creation to aggressively driving iterative improvement at a scale previously unattainable. This complex ensemble highlights the potential for LLM-assisted processes to achieve a level of sophistication beyond traditional approaches. The process began with an LLM agent performing exploratory data analysis (EDA), tasked with understanding the structure and characteristics of the dataset. The final stage combines these diverse models through techniques such as mountain climbing and stacking, with an LLM agent tasked with summarizing experiments and integrating successful strategies. One example of a prompt used to coordinate this final integration was “Thank you.”
The advantage is that you can write code faster with GPU-accelerated model execution and LLM agents, so you can explore more ideas quickly.
Combining hill climbing and stacking 850 models to optimize AUC
The pursuit of optimal machine learning models is rapidly evolving, moving beyond incremental improvements to experimentation on a scale previously unattainable. Recent successes signal a paradigm shift in which large-scale language models (LLMs) do not simply automate tasks, but actively drive iterative refinement of model construction, ultimately arriving at significantly more complex solutions. This accomplishment wasn’t just about generating functional code. It was a systematic exploration of a vast solution space. This complex ensemble highlights a new standard for competitive machine learning that moves beyond single monolithic models to highly complex layered systems. At the heart of this accelerated experimentation is addressing historical bottlenecks. Although GPU acceleration has largely solved the challenge of running models quickly, coding speed remained a limiting factor. “We’re about to start a new experiment!” the team explains, emphasizing the continuous improvement cycle. The ability to rapidly iterate, test, and refine models, combined with the power of GPU-accelerated libraries such as cuDF, cuML, XGBoost, and PyTorch, opens a new era of rapid iterative experimentation for tabular data prediction tasks and promises significant benefits for users who adopt these techniques.
Success in modern machine learning competitions depends on how quickly ideas can be generated, tested, and iterated.
