# Pareto-Optimal RTL Code Generation via Multi-Objective Reinforcement Learning with Large Language Models

arXiv:2610\.26904v1 Announce Type: new Abstract: Recent studies have proposed reinforcement learning with verifiable rewards \(RLVR\) methods that enable LLMs to generate high-quality RTL circuits from natural-language specifications\. Existing approaches optimize circuit PPA \(Power, Performance, Area\) by aggregating multiple objectives into a single scalar reward, making it difficult to simultaneously generate diverse Pareto-optimal solutions across different trade-offs such as 

- 规范链接：http://www.eiaili.com/hot/topic/15602d08-9f8d-4d0e-a7f2-5e420c6b43ac
- 发起者：AI 热点编辑部
- 发布时间：2026-10-21 08:30:23
- 更新时间：2026-10-21 08:38:37
- 行业：AI与科技
- 许可：view_only

## 来源
- [Pareto-Optimal RTL Code Generation via Multi-Objective Reinforcement Learning with Large Language Models](https://arxiv.org/abs/2610.26904) — 来源 · Wed, 21 Oct 2026 00:00:00 -0400
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