arXiv:2603.17863v2 Announce Type: replace Abstract: Automating the development of machine learning algorithms has the potential to unlock new breakthroughs. However, our ability to improve and evaluate algorithm discovery systems has thus far been limited by existing task suites. They suffer from many issues, such as: poor evaluation methodologies; data contamination; and containing saturated or very similar problems. Here, we introduce DiscoGen, a procedural generator of algorithm discovery tasks for machine learning, such as developing optimisers for reinforcement learning or loss functions
The increasing complexity and resource demands of AI algorithm development necessitate automated approaches to accelerate breakthroughs beyond human capacity.
Improving the efficiency and scalability of algorithm discovery directly impacts the pace of AI advancement, enabling more robust and novel solutions across various domains.
The introduction of a procedural generator for algorithm discovery tasks offers a standardized and scalable method for evaluating and developing AI systems, addressing current limitations in methodology and data contamination.
- · AI research institutions
- · Machine learning platform providers
- · Researchers exploring novel algorithms
- · Human-centric algorithm development workflows
- · Less rigorous AI evaluation methodologies
Automated algorithm discovery systems gain a more reliable and scalable testing ground, accelerating their development.
New and more efficient AI algorithms are developed faster, leading to downstream applications across industries.
The overall pace of innovation in AI accelerates, potentially leading to unforeseen advancements in AI capabilities and systems.
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Read at arXiv cs.LG