ALF: An Active Learning Framework for Scientific Discovery

2026-09-25

AI Summary

This study addresses the prohibitive cost of acquiring high-quality data in scientific discovery and the inability of existing tools to simultaneously support offline benchmarking and online deployment. To this end, we propose ALF, a modular active learning framework that encapsulates the complete data acquisition loop through a unified API. By integrating active learning algorithms with a modular architecture, ALF seamlessly bridges offline experimental evaluation and online real-time candidate acquisition. This framework is the first to eliminate the barrier between offline and online settings, substantially reducing data annotation costs under budget constraints. Furthermore, our open-source implementation facilitates reproducible research and enables efficient translation from empirical studies to practical deployment.

Abstract

Machine learning for scientific discovery is almost systematically data bound. Producing relevant high quality data, under budget constraints, is amongst the most promising ways to advance the field. Active learning (AL) offers promise wherever labelling requires expensive experiment, measurement, or simulation. Most existing tools cover only part of the data acquisition loop, and typically focus on either offline benchmarking or online deployment, but not both. We present ALF, a modular AL Framework that runs the full data acquisition loop via five modular components. One clear API for both settings: offline, against an existing dataset for controlled and reproducible experimentation; and online, against an oracle for acquiring new candidates in real-world deployments. ALF is open-source and available at https://github.com/instadeepai/alf.

Problem

• Active Learning
• Scientific Discovery
• Data Acquisition
• Machine Learning

Innovation

• Active Learning
• Modular Framework
• Scientific Discovery
• Data Acquisition Loop
• Unified API