arXiv AI October 21, 2026 By Koki Takeshita, Kenji Uebayashi, Hikaru Tomonari, Masaya Tsunokake, Koichiro Yawata, Tatsuya Sasaki, Yusuke Ohtsubo, Kota Dohi, Yasuhiro Sogawa

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

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The paper introduces a reinforcement learning framework that uses large language models to generate RTL circuits while optimizing for Pareto-optimality across power, performance, and area (PPA). It proposes a generation-based training pipeline and a Hypervolume Contribution reward to improve both the quality and diversity of the entire set of generated circuits. Experiments on the RTLLM v2 benchmark show significant gains in hypervolume and diversity compared to baseline and single-objective methods.

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