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Life sciences · Preprint
arXiv · September 8, 2026
Early or partial results. Treat as a signal, not a conclusion.
This is an unreviewed preprint describing an offline reinforcement learning policy using LSTM to predict routing cost weights in dense electronic circuit layouts. The work reports a 92% reduction in design rule violations and 10% runtime reduction relative to a public baseline on held-out test cases, but lacks peer review, independent validation, and detailed sample size reporting.
Uncontrolled algorithm evaluation on held-out test sets. Electronic circuit layouts (dense placement regimes); no description of number or diversity of test designs provided.. Intervention: History-aware offline RL policy with LSTM predicting iterative cost weights for routing. Compared with: Top public baseline router (unspecified).
Policy reduces design rule violations (DRVs) by an average of 92% over the top public baseline Simultaneously reduces runtime by 10% LSTM architecture with additional features improves routing convergence across multiple densities and route guide qualities
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An unreviewed technical method paper presenting a single-arm engineering solution without peer review, comparative validation across independent datasets, or clinical/biological endpoints.
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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 placement densities by utilizing readily available features from the router. Our policy uses conservative Q-learning similarly to prior work; however, our key insight is that addition of a lightweight LSTM architecture and additional features can retain sequence context and improve routing convergence across multiple densities and route guide qualities. Our policy can be integrated into any cost-based router with minimal pipeline changes, as it does not interfere with the core search algorithm. We evaluate our policy on held-out density and adjustment settings, including difficult operating points induced by dense placement and low guide quality. Our policy reduces design rule violations (DRVs) by an average of 92% over the top public baseline while simultaneously reducing runtime by 10%.
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