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Routing Dense Layouts with History-Aware Offline Reinforcement Learning using LSTM

arXiv · AI, language, vision and robotics · article · Sep 8, 2026 · UTC

Detailed routing remains a dominant runtime bottleneck in physical design due to increasing complexity of design rules. Modern routers can struggle to resolve persistent violations under dense operating conditions. While recent work leverages reinforcement learning (RL) to dynamically select costs for each routing iteration, we find that this technique struggles with high-density designs where routing solutions are significantly harder. To address this, we present a history-aware offline RL policy which predicts iterative cost weights in these dense regimes to improve convergence across placem

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