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[2505.24183] QiMeng-CodeV-R1: Reasoning-Enhanced Verilog Generation
Summary
The paper introduces QiMeng-CodeV-R1, a framework for reasoning-enhanced Verilog generation using reinforcement learning with verifiable rewards, addressing key challenges in electronic design automation.
Why It Matters
This research is significant as it advances the field of electronic design automation (EDA) by improving the generation of hardware description languages (HDLs) like Verilog. By overcoming challenges such as verification and dataset quality, it paves the way for more efficient and reliable hardware design processes, which are crucial in the era of rapid technological advancement.
Key Takeaways
- Introduces CodeV-R1, a novel RLVR framework for Verilog generation.
- Develops a rule-based testbench generator for robust equivalence checking.
- Implements a round-trip data synthesis method to enhance dataset quality.
- Achieves significant performance improvements over prior models in Verilog generation tasks.
- Releases model, training code, and dataset to support further research.
Computer Science > Machine Learning arXiv:2505.24183 (cs) [Submitted on 30 May 2025 (v1), last revised 23 Feb 2026 (this version, v5)] Title:QiMeng-CodeV-R1: Reasoning-Enhanced Verilog Generation Authors:Yaoyu Zhu, Di Huang, Hanqi Lyu, Xiaoyun Zhang, Chongxiao Li, Wenxuan Shi, Yutong Wu, Jianan Mu, Jinghua Wang, Yang Zhao, Pengwei Jin, Shuyao Cheng, Shengwen Liang, Xishan Zhang, Rui Zhang, Zidong Du, Qi Guo, Xing Hu, Yunji Chen View a PDF of the paper titled QiMeng-CodeV-R1: Reasoning-Enhanced Verilog Generation, by Yaoyu Zhu and 18 other authors View PDF HTML (experimental) Abstract:Large language models (LLMs) trained via reinforcement learning with verifiable reward (RLVR) have achieved breakthroughs on tasks with explicit, automatable verification, such as software programming and mathematical problems. Extending RLVR to electronic design automation (EDA), especially automatically generating hardware description languages (HDLs) like Verilog from natural-language (NL) specifications, however, poses three key challenges: the lack of automated and accurate verification environments, the scarcity of high-quality NL-code pairs, and the prohibitive computation cost of RLVR. To this end, we introduce CodeV-R1, an RLVR framework for training Verilog generation LLMs. First, we develop a rule-based testbench generator that performs robust equivalence checking against golden references. Second, we propose a round-trip data synthesis method that pairs open-source Verilog snippets...
