LLM Evolves Algorithms to Improve Circle Packing
💡See how $27.72 of LLM calls improved 10 established circle-packing solutions.
⚡ 30-Second TL;DR
What Changed
The LLM evolves the solver algorithm rather than directly generating circle placements.
Why It Matters
The result suggests that LLMs can contribute to algorithm discovery and optimization-loop design, not only code generation. If reproducible, this approach could offer a relatively low-cost way to search heuristic improvements for difficult optimization problems.
What To Do Next
Clone the Discovery Loop repository and reproduce one Packomania csqv improvement with the independent verifier before adapting the loop to your own optimizer.
Key Points
- •The LLM evolves the solver algorithm rather than directly generating circle placements.
- •Independent scoring and verification discard failed candidates and preserve improvements.
- •The method improved 10 Packomania csqv instances, with gains ranging from 2.4% to 5.4%.
- •The experiment used 15 iterations and cost $27.72 in total LLM usage.
- •Packomania independently accepted the submitted results.
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Original source: Reddit r/MachineLearning ↗
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