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<!DOCTYPE html>
<html>
<head>
<script defer src="https://cloud.umami.is/script.js" data-website-id="028ba410-097e-443c-a63b-7e6f79597d30"></script>
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<!-- Meta tags for social media banners, these should be filled in appropriatly as they are your "business card" -->
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<meta name="description" content="KodCode">
<meta property="og:title" content="KodCode"/>
<meta property="og:description" content="A Diverse, Challenging, and Verifiable Synthetic Dataset for Coding"/>
<meta property="og:url" content="https://kodcode-ai.github.io/"/>
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<meta name="keywords" content="Large Language Models, Synthetic Dataset, Coding, Post-training">
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<title>KodCode</title>
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<div class="container is-max-desktop">
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<div class="column has-text-centered">
<h1 class="title is-1 publication-title">KodCode: A Diverse, Challenging, and Verifiable Synthetic Dataset for Coding</h1>
<div class="is-size-5 publication-authors">
<!-- Paper authors -->
<span class="author-block">
<a href="https://zhangchenxu.com/" target="_blank">Zhangchen Xu</a><sup>*</sup>,</span>
<span class="author-block">
<a href="https://nlp-yang.github.io/" target="_blank">Yang Liu</a>,</span>
<span class="author-block">
<a href="https://scholar.google.com/citations?user=HhEo-1cAAAAJ&hl=en" target="_blank">Yueqin Yin</a><sup>*</sup>,</span>
<span class="author-block">
<a href="https://mingyuanzhou.github.io/" target="_blank">Mingyuan Zhou</a>,</span>
<span class="author-block">
<a href="https://people.ece.uw.edu/radha/" target="_blank">Radha Poovendran</a>,</span>
</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block">Microsoft GenAI, University of Washington, The University of Texas at Austin</span>
<span class="eql-cntrb"><small><br><sup>*</sup>Work done during internship at Microsoft GenAI</small></span>
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</span>
<span>Paper</span>
</a>
</span>
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<span class="link-block">
<a href="https://huggingface.co/KodCode" target="_blank"
class="external-link button is-normal is-rounded is-dark">
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🤗
</span>
<span>HF Organization</span>
</a>
</span>
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<span class="link-block">
<a href="https://huggingface.co/datasets/KodCode/KodCode-V1" target="_blank"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fas fa-database"></i>
</span>
<span>KodCode-V1 (RL)</span>
</a>
</span>
<!-- HuggingFace Dataset link -->
<span class="link-block">
<a href="https://huggingface.co/datasets/KodCode/KodCode-V1-SFT-R1" target="_blank"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fas fa-database"></i>
</span>
<span>KodCode-V1-SFT-R1</span>
</a>
</span>
<!-- Github link -->
<span class="link-block">
<a href="https://github.com/KodCode-AI/kodcode" target="_blank"
class="external-link button is-normal is-rounded is-dark">
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<i class="fab fa-github"></i>
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<span>Codebase</span>
</a>
</span>
<!-- ArXiv abstract Link -->
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<h2 class="title is-3">Abstract</h2>
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<p>
We introduce <b>KodCode</b>, a <b>synthetic dataset</b> that addresses the persistent challenge of acquiring <b>high-quality, verifiable training data</b> across <b>diverse difficulties and domains</b> for training Large Language Models for coding. Existing code-focused resources typically fail to ensure either the breadth of coverage (e.g., spanning simple coding tasks to advanced algorithmic problems) or verifiable correctness (e.g., unit tests). In contrast, KodCode comprises <b>question-solution-test triplets</b> that are systematically validated via a self-verification procedure. Our pipeline begins by synthesizing a broad range of coding questions, then generates solutions and test cases with additional attempts allocated to challenging problems. Finally, post-training data synthesis is done by rewriting questions into diverse formats and generating responses under a test-based reject sampling procedure from a reasoning model (DeepSeek R1). This pipeline yields a large-scale, robust and diverse coding dataset. KodCode is suitable for supervised fine-tuning and the paired unit tests also provide great potential for RL tuning. Fine-tuning experiments on coding benchmarks (HumanEval(+), MBPP(+), BigCodeBench, and LiveCodeBench) demonstrate that KodCode-tuned models achieve state-of-the-art performance, surpassing models like Qwen2.5-Coder-32B-Instruct and DeepSeek-R1-Distill-Llama-70B.
</p>
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<div class="has-text-centered">
<h2 class="title is-3">KodCode Pipeline</h2>
</div>
<br>
<!-- Figure on top -->
<figure class="image" style="width: 95%; margin: 0 auto;">
<img src="static/images/kodcode-pipeline.jpg" alt="" />
</figure>
<br>
<p class="has-text-left">
This figure demonstrates the pipeline for generating KodCode-V1. Our approach follows a three-step pipeline: <i>Coding Question Synthesis</i>, <i>Solution & Test Generation</i>, and <i>Post-training Data Synthesis</i>. The final KodCode-V1 dataset contains <b>447K verified question-solution-test triplets</b>. The distribution of each subset is demonstrated on the right.
</p>
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<h2 class="title is-3">✨ KodCode is Diverse! ✨</h2>
</div>
<br>
<!-- Figure on top -->
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<img src="static/images/diversity.jpg" alt="" />
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<br>
<p class="has-text-left">
This figure demonstrates the diversity of KodCode-V1. We present statistics on questions, solutions, and number of tests, as well as the distribution of each subset compared with baselines.
</p>
</div>
</section>
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<div class="container is-max-desktop">
<!-- Optional section title -->
<div class="has-text-centered">
<h2 class="title is-3">✨ KodCode is Challenging! ✨</h2>
</div>
<br>
<!-- Figure on top -->
<figure class="image" style="width: 95%; margin: 0 auto;">
<img src="static/images/challenging.jpg" alt="" />
</figure>
<br>
<p class="has-text-left">
This figure shows the difficulty of KodCode-V1. Importantly, we found that allocating more attempts to challenging problems is effective in improving pass rates for these problems.
</p>
</div>
</section>
<section class="section"></section>
<div class="container is-max-desktop">
<!-- Optional section title -->
<div class="has-text-centered">
<h2 class="title is-3">✨ Performance on Coding Benchmarks ✨</h2>
</div>
<br>
<!-- Figure on top -->
<figure class="image" style="width: 95%; margin: 0 auto;">
<img src="static/images/performance comparison.png" alt="" />
</figure>
<br>
<p class="has-text-left">
This figure shows the performance of KodCode-SFT on coding benchmarks. We compare KodCode with SOTA non-reasoning and reasoning models on HumanEval(+), MBPP(+), BigCodeBench, and LiveCodeBench.
</p>
<br>
</div>
</section>
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<!-- Kodkod introduction and video -->
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<h2 class="title is-3">🐱 Meet Kodkod: Nature's Stealthy Programmer! 🐾</h2>
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<div class="content has-text-justified">
<p>
Kodkod (Leopardus guigna) is a wild cat species native to the Americas, known for its adaptability and resilience in various environments. Just as the Kodkod navigates through diverse habitats, from dense forests to open areas, KodCode is designed to handle various programming challenges with agility and precision.
</p>
</div>
<div class="publication-video">
<!-- Youtube embed code here -->
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Why we name it KodCode?
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<pre><code>@article{xu2024kodcode,
title={KodCode: A Diverse, Challenging, and Verifiable Synthetic Dataset for Coding},
author={Zhangchen Xu and Yang Liu and Yueqin Yin and Mingyuan Zhou and Radha Poovendran},
}</code></pre>
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