KodCode: A Diverse, Challenging, and Verifiable Synthetic Dataset for
  Coding
Authors: name: Zhangchen Xu, name: Yang Liu, name: Yueqin Yin, name: Mingyuan Zhou, name: Radha Poovendran

We introduce KodCode, a synthetic dataset that addresses the persistent
challenge of acquiring high-quality, verifiable training data across diverse
difficulties and domains 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
question-solution-test triplets 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.