Simple baselines match complex code evolution methods, study finds

A new research paper titled "Simple Baselines are Competitive with Code Evolution" challenges the complexity of existing code evolution techniques. The study proposes two simple baselines that surprisingly match or outperform more intricate pipelines across various domains, including mathematical bounds, agentic scaffolds, combinatorial optimization, and machine learning competitions. The findings suggest that the performance of code evolution methods is more dependent on the surrounding setup, such as search space and prompt engineering, rather than the complexity of the pipeline itself. The research advocates for improved evaluation procedures to mitigate stochasticity and ensure meaningful progress in the field. AI

IMPACT Suggests that simpler setups and improved evaluation procedures are key to advancing code evolution, rather than complex algorithms.

RANK_REASON Research paper published on arXiv discussing baselines for code evolution techniques. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• Code Evolution
• Hugging Face
• large-language models
• Simple Baselines are Competitive with Code Evolution
• Yonatan Gideoni

• paper
• other

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