芯片设计团队终于有了可私有化部署的RTL生成方案——ChipMATE不依赖闭源API、无需黄金测试平台，还能用厂商内部代码训练，做数字IC设计的开发者可以直接试。
ChipMATE是首个自训练的多智能体RTL代码生成框架，解决了现有API系统依赖黄金测试平台、无法在气隙环境中部署、不能利用厂商私有代码库的问题。它通过Verilog智能体与Python参考模型智能体相互验证，无需黄金标准即可实现正确性。采用回溯推理工作流防止错误传播，两阶段训练先独立训练再联合协作。在VerilogEval V2上，4B和9B模型分别达到75.0%和80.1%的pass@1，超越所有自训练模型甚至1600B参数的DeepSeek V4。代码和权重已开源。
ChipMATE: Multi-Agent Training via Reinforcement Learning for Enhanced RTL Generation
Existing API-based agentic systems for RTL code generation are fundamentally misaligned with industrial practice: they assume a golden testbench is available at generation time, rely on closed-source APIs incompatible with chip vendors' air-gapped security requirements, and cannot be trained on vendors' proprietary RTL codebases, leaving valuable internal data unused. Recent self-trained models address the deployment constraint but remain single-turn generators that overlook the critical role of verification in real industrial flows. To bridge these gaps, we present ChipMATE, the first self-trained multi-agent framework for RTL generation. Inspired by industrial practice where correctness emerges from cross-comparison between independently written RTL modules and reference models, ChipMATE pairs a Verilog agent with a Python reference-model agent that mutually verify each other's outputs without any golden oracle. We design a backtrack-based inference workflow to prevent error propagation across turns, and a two-stage training pipeline that first trains each agent individually to saturate its code-generation capability, then trains the team jointly to collaborate effectively. To support the training, we further build a hybrid data-generation framework that produces 64.4K high-quality reference model training samples. ChipMATE achieves 75.0\% and 80.1\% pass@1 on VerilogEval V2 with 4B and 9B base models, outperforming all existing self-trained models and even DeepSeek V4 with 1600B parameters. Our code and model weights are publicly available in https://github.com/zhongkaiyu/ChipMATE.