Recent advances in large language models (LLMs) have sparked growing interest in automatic RTL optimization for better performance, power, and area (PPA). However, existing methods are still far from realistic RTL optimization. Their evaluation settings are often unrealistic: they are tested on manually degraded, small-scale RTL designs and rely on weak open-source tools. Their optimization methods are also limited, relying on coarse design-level feedback and simple pre-defined rewriting rules. To address these limitations, we present Dr. RTL, an agentic framework for RTL timing optimization in a realistic evaluation environment, with continual self-improvement through reusable optimization skills. We establish a realistic evaluation setting with more challenging RTL designs and an industrial EDA workflow. Within this setting, Dr. RTL performs closed-loop optimization through a multi-agent framework for critical-path analysis, parallel RTL rewriting, and tool-based evaluation. We further introduce group-relative skill learning, which compares parallel RTL rewrites and distills the optimization experience into an interpretable skill library. Currently, this library contains 47 pattern--strategy entries for cross-design reuse to improve PPA and accelerate convergence, and it can continue evolving over time. Evaluated on 20 real-world RTL designs, Dr. RTL achieves average WNS/TNS improvements of 21%/17% with a 6% area reduction over the industry-leading commercial synthesis tool.

翻译：近年来，大语言模型（LLM）的进展引发了通过自动RTL优化实现更优性能、功耗和面积（PPA）的广泛关注。然而，现有方法距离实际RTL优化仍有显著差距。其评估设置往往不切实际：测试对象多为人工降质的小规模RTL设计，且依赖性能较弱的开源工具。优化方法亦存在局限，仅依赖粗粒度的设计级反馈和简单的预定义重写规则。为突破上述瓶颈，我们提出Dr. RTL——一种面向真实评估环境的RTL时序优化智能体框架，通过可复用的优化技能实现持续自我改进。我们构建了包含更具挑战性的RTL设计与工业级EDA流程的真实评估场景。在此场景下，Dr. RTL通过多智能体框架实现关键路径分析、并行RTL重写与基于工具的评估的闭环优化。我们进一步引入群体相对技能学习机制，通过比较并行RTL重写结果，将优化经验提炼为可解释的技能库。当前该库包含47个模式-策略条目，支持跨设计复用以优化PPA并加速收敛，且能随时间持续演化。在20个真实RTL设计上的评估表明，相比业界领先的商业综合工具，Dr. RTL平均实现WNS/TNS改进21%/17%，面积缩减6%。