Scattered Forest Search: Smarter Code Space Exploration with LLMs
Keywords: LLM, code generation, optimization, search, agent, inference scaling
TL;DR: We propose a new method to encourage more exploration from LLMs when search for solutions across code space
Abstract: We propose a novel approach to scaling LLM inference for code generation. We
frame code generation as a black box optimization problem within the code space,
and employ optimization-inspired techniques to enhance exploration. Specifically,
we introduce SCATTERED FOREST SEARCH to enhance solution diversity while
searching for solutions. Our theoretical analysis illustrates how these methods avoid
local optima during optimization. Extensive experiments on HumanEval, MBPP,
APPS, CodeContests, and Leetcode reveal significant performance improvements.
For instance, our method achieves a pass@1 rate of 67.1% on HumanEval+ and
87.2% on HumanEval with GPT-3.5, marking improvements of 8.6% and 4.3%
over the state-of-the-art, while also halving the iterations needed to find the correct
solution. Furthermore, our method scales more efficiently than existing search
techniques, including tree search, line search, and repeated sampling.
Primary Area: applications to computer vision, audio, language, and other modalities
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Submission Number: 12517
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