[Submitted on 16 Oct 2026]
Title:Pareto-Optimal RTL Code Generation via Multi-Objective Reinforcement Learning with Large Language Models
View a PDF of the paper titled Pareto-Optimal RTL Code Generation via Multi-Objective Reinforcement Learning with Large Language Models, by Koki Takeshita and 8 other authors
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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 low power, low latency, and small area. However, in practical design, the relative importance of each objective varies depending on product requirements and design constraints; thus, obtaining a diverse and high-quality candidate set is beneficial in that designers can choose a preferred trade-off a posteriori. In this work, we are the first to propose a learning method for LLM-based circuit generation that optimizes, not the quality of a single circuit, but the quality and diversity of the entire circuit set obtained through multiple generations in the PPA objective space. The proposed method consists of (1) a generation-based training pipeline that reformulates coverage improvement of the entire Pareto solution set into a sequential circuit generation problem, and (2) a reward function based on Hypervolume Contribution (HVC) that measures how much a generated circuit contributes to the Pareto front. Evaluation on the RTLLM v2 benchmark shows that the proposed method achieves +13.3 percentage points in relative Hypervolume over a baseline without PPA optimization and +14.4 percentage points over a single-objective reinforcement learning method, while also demonstrating superiority in circuit diversity.
Comments: Accepted at COLM 2026
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2610.26904 [cs.AI]
(or arXiv:2610.26904v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.26904
arXiv-issued DOI via DataCite
Submission history
From: Koki Takeshita [view email] [v1] Fri, 16 Oct 2026 22:58:25 UTC (2,877 KB)
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