From 56292f0febc633d5e3c95a1cb57bcf5ba314c255 Mon Sep 17 00:00:00 2001 From: Ryan Ehrlich Date: Sun, 26 Jan 2025 20:01:46 -0800 Subject: [PATCH] Add CodeMonkeys paper --- Gemfile.lock | 1 + _pubs/codemonkeys.md | 48 +++++++++++++++++++++++++++++++++++ imgs/teasers/codemonkeys.png | Bin 0 -> 338096 bytes imgs/thumbs/codemonkeys.png | Bin 0 -> 721913 bytes pubs/codemonkeys.pdf | Bin 0 -> 1484311 bytes 5 files changed, 49 insertions(+) create mode 100644 _pubs/codemonkeys.md create mode 100644 imgs/teasers/codemonkeys.png create mode 100644 imgs/thumbs/codemonkeys.png create mode 100644 pubs/codemonkeys.pdf diff --git a/Gemfile.lock b/Gemfile.lock index e079d98..8096488 100644 --- a/Gemfile.lock +++ b/Gemfile.lock @@ -105,6 +105,7 @@ GEM PLATFORMS arm64-darwin-21 arm64-darwin-23 + arm64-darwin-24 x64-mingw-ucrt x86_64-darwin-19 x86_64-darwin-20 diff --git a/_pubs/codemonkeys.md b/_pubs/codemonkeys.md new file mode 100644 index 0000000..58583bd --- /dev/null +++ b/_pubs/codemonkeys.md @@ -0,0 +1,48 @@ +--- +title: 'CodeMonkeys: Scaling Test-Time Compute for Software Engineering' +authors: + - key: ryanehrlich + equal: true + - key: bradleybrown + affiliation: University of Oxford + equal: true + - key: jordanjuravsky + equal: true + - name: Ronald Clark + affiliation: University of Oxford + - name: Christopher RĂ© + affiliation: Stanford + - key: azaliamirhoseini +venue: preprint +year: 2025 +day: 23 +has_pdf: true +doi: 10.48550/arXiv.2501.14723 +tags: + - machine learning + - generative AI +teaser: CodeMonkeys, a system designed to solve software engineering problems by scaling test time compute. +materials: + - name: Paper + url: https://arxiv.org/abs/2501.14723 + type: file-pdf + - name: CodeMonkeys Codebase + url: https://github.com/ScalingIntelligence/codemonkeys + type: code + - name: Trajectories + url: https://github.com/swe-bench/experiments/pull/171 + type: database + - name: Codebase Content Dataset + url: https://huggingface.co/datasets/ScalingIntelligence/swe-bench-verified-codebase-content + type: database +--- +Scaling test-time compute is a promising axis for improving LLM capabilities. +However, test-time compute can be scaled in a variety of ways, and effectively combining different approaches remains an active area of research. +Here, we explore this problem in the context of solving real-world GitHub issues from the SWE-bench dataset. +Our system (CodeMonkeys) allows models to iteratively edit a codebase by jointly developing and running a testing script alongside their draft edit. +We sample many of these multi-turn trajectories for every issue to generate a collection of candidate edits. +This approach lets us scale "serial" test-time compute by increasing the number of iterations per trajectory and "parallel" test-time compute by increasing the number of trajectories per problem. +With parallel scaling, we can amortize up-front costs across multiple downstream samples, allowing us to identify relevant codebase context using the simple method of letting an LLM read every file. +In order to select between candidate edits, we combine voting with model-generated tests with a final multi-turn trajectory dedicated to selection. +Overall, CodeMonkeys resolves 57.7% of issues from SWE-bench Verified using a budget of approximately 2300 USD. +Our selection method can also be used to combine candidates from different sources. 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