技术规格

Kimi-Dev-72B

Kimi-Dev-72B is a specialized large language model developed by Moonshot AI, engineered specifically for autonomous software engineering and complex issue resolution. Built upon the Qwen2.5-72B foundational architecture, the model undergoes a sophisticated multi-stage training process designed to instill structured skill priors for software development tasks. This process includes a large-scale mid-training phase using approximately 150 billion tokens of high-quality, real-world data from GitHub issues and pull request commits, enabling the model to internalize the reasoning patterns and technical workflows employed by human developers. Unlike general-purpose coding assistants, Kimi-Dev-72B is optimized to function as an autonomous agent capable of localized file identification and precise code editing.

The model's core innovation lies in its duo-stage framework, comprising specialized "BugFixer" and "TestWriter" behaviors. This architecture facilitates a two-step operational cycle: first, the model identifies the relevant files within a repository (File Localization), and second, it generates the necessary code modifications or unit tests (Code Edits). The training methodology leverages large-scale reinforcement learning (RL) with outcome-based rewards, where the model receives positive reinforcement only when its proposed patches successfully pass an entire test suite within a containerized Docker environment. This rigorous verification loop ensures that the generated solutions are functionally correct and adhere to production-grade standards.

Kimi-Dev-72B is designed for seamless integration into modern software development lifecycles, supporting tasks such as automated bug fixing, unit test generation, and comprehensive code reviews. By employing a test-time self-play mechanism, the model iteratively refines its outputs, making it highly effective for resolving complex issues in large-scale codebases. Its dense 72-billion-parameter architecture provides a robust balance between reasoning capability and computational efficiency, while its 131,072-token context window allows it to maintain a deep understanding of extensive project structures and cross-file dependencies. The model is released under the MIT license, providing the community with open access to its weights and source code for further research and development.

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