Simple Baselines Rival Code Evolution
💡Simple baselines beat complex code evolution—rethink your LLM search strategies & save compute!
⚡ 30-Second TL;DR
What Changed
Simple baselines exceed code evolution in finding math bounds, agent scaffolds, ML competitions
Why It Matters
This challenges reliance on sophisticated LLM code search methods, promoting simpler, efficient baselines that save compute. It urges better domain expertise in prompts and evaluations, potentially accelerating practical AI code generation.
What To Do Next
Implement random mutation baseline in your next LLM code search experiment before scaling to evolution pipelines.
Key Points
- •Simple baselines exceed code evolution in finding math bounds, agent scaffolds, ML competitions
- •Search space design and prompt domain knowledge dictate performance more than pipelines
- •High scaffold variance with small datasets favors hand-designed majority vote
- •Proposes low-stochasticity evaluations for feasible code evolution
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Original source: ArXiv AI ↗
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