RTL-Interact: Training EDA-Interactive Models for Multi-Task RTL Design

Authors: Wenxuan Shi , Yaoyu Zhu , Di Huang , Chongxiao Li , Xiaoyun Zhang , Muxin Song , Yutong Wu , Ziyuan Nan , Pengwei Jin , Tian Zhi , Xishan Zhang , Rui Zhang , Xing Hu , Zidong Du , Qi Guo , Yunji Chen

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Abstract: Large language models offer a promising route to register-transfer-level (RTL) design, but reliable implementations require checking and revision based on electronic design automation (EDA) tools' feedback. Existing RTL-specialized models often lack learned interaction with such EDA tools or rely on separate generation and repair components. We introduce RTL-Interact, a multi-task post-training framework that trains a single RTL-specialized model to generate RTL, construct task-appropriate EDA checks, and revise implementations using tool feedback. The framework combines automatically constructed multi-task demonstrations with staged supervision from chain-of-thought to tool-integrated reasoning. We further introduce Pattern-Aware Policy Optimization (PAPO) for multi-task reinforcement learning. PAPO augments Group Relative Policy Optimization with correctness-gated rewards that favor concise successful checking while rewarding effective revision, and uses task grouping to preserve coverage across objectives. On CVDP, RTL-Interact-32B achieves 48.7% weighted Pass@1 and 68.9% Pass@5, surpassing its teacher model, DeepSeek-V3.2, while RTL-Interact-8B achieves 44.9% weighted Pass@1. Additionally, we show that PAPO can reduce average tool calls by 63.8% while improving Pass@1. Both models also outperform all RTL-specialized baselines on VerilogEval v2 and RTLLM v2, demonstrating the effectiveness of our method.

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Under review at ICLR 2027

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Artificial Intelligence (cs.AI)

Cite as:

arXiv:2610.41225 [cs.AI]

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arXiv:2610.41225v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.41225

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arXiv-issued DOI via DataCite (pending registration)

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From: Wenxuan Shi [ view email ]

[v1]

Fri, 30 Oct 2026 04:03:39 UTC (281 KB)

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