In short: Google researchers have developed Mle-Star, an automatic learning agent that improves the AI model creation process by combining targeted web search, code refinement and adaptive assembly. Mle-Star has demonstrated its effectiveness by winning 63% of the Benchmark Mle-Bench-Lite Benchmark based on Kaggle, largely surpassing previous approaches.

Mle agents (Machine Learning Engineering agent), based on major language models (LLMS), have opened new perspectives in the development of automatic learning models by automating all or part of the process. However, existing solutions often come up against exploration limits or a lack of methodological diversity. Google researchers respond to these challenges with Mle-Star, an agent who combines targeted web search, granular refinement of code blocks and adaptive assembly strategy.

Concretely, an agent Mle starts from a task description (for example, “predict sales from tabular data”) and sets of data provided, then:

Analyzes the problem and chooses an appropriate approach;
Generates code (often in python, with common or specialized ML libraries);
Test, assess and refine the solution, sometimes in several iterations.

These agents rely on two key skills of LLM:

Algorithmic reasoning (identify relevant methods for a given problem);
The generation of executable code (complete data preparation, training and evaluation scripts).

Their objective is to reduce the human workload by automating tedious steps such as characteristics engineering, hyperparameter adjustment or model selection.

Mle-Star: targeted and iterative optimization

According to Google Research, the existing Mle come up against two major obstacles. First of all, their high dependence on the internal knowledge of LLMS pushes them to favor generic and well-established methods, such as the Scikit-Learn library for tabular data, to the detriment of more specialized and potentially more efficient approaches.
Then, their exploration strategy is often based on a complete rewriting of the code with each iteration. This operation prevents them from concentrating their efforts on specific components of the pipeline, for example, systematically test different characteristics engineering options, before moving on to other stages.

To exceed these limits, Google researchers have designed Mle-Star, an agent who combines three levers:

Web search to identify specific models and constitute a solid initial solution;
Granular refinement by code blocks, based on ablation studies to identify the parties with the most impact on performance, then by optimizing them iteratively;
Adaptive assembly strategy, capable of merge several candidate solutions into an improved version, refined over the attempts.

This iterative process, research, identification of the critical block, optimization, then new iteration, allows Mle-Star to concentrate its efforts where they produce the most measurable gains.

Preview. a) Mle-Star begins by using search on the web to find and incorporate models specific to a task in an initial solution. (b) For each step of refinement, he performs a removal study in order to determine the code block with the most significant impact on performance. (c) The identified code block then undergoes an iterative refinement based on the plans suggested by LLM, which explore various strategies using the comments of previous experiences. This process of selection and refinement of target code blocks is repeated, where the improved solution of (c) becomes the starting point for the next refinement stage (b).

Control modules to make solutions reluctantly

Beyond its iterative approach, Mle-Star incorporates three modules intended to strengthen the robustness of the solutions generated:

A debugging agent To analyze execution errors (for example, a traceback Python) and offer automatic corrections;
A data leak auditor pour Detecting situations where information from test data is used wrongly during training, a bias that distorts the measured performance;
A data use verifier To ensure that all data sources provided are used, even when they do not arise in standard formats such as the CSV.

These modules respond to current problems observed in the code generated by LLMS.

Significant results on Kaggle

To assess the efficiency of Mle-Star, the researchers tested it as part of the Benchmark Mle-Bench-Lite, based on Kaggle competitions. The protocol measured the ability of an agent to produce, from a simple task description, a complete and competitive solution.

The results show that Mle-Star obtains a medal in 63 % of competitions, 36 % in gold, compared to 25.8 % to 36.6 % for the best previous approaches. This gain is attributed to the combination of several factors: the rapid adoption of recent models like EfficientNet or VIE, the ability to integrate models unidentified by web research through punctual human intervention, and the automatic corrections made by leak and data use auditors.

A new approach for machine learning engineering

Mle-Star: targeted and iterative optimization

Control modules to make solutions reluctantly

Significant results on Kaggle

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