Hugging Face Trending Papers September 8, 2026

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

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The paper introduces a history‑aware offline reinforcement learning policy that predicts iterative cost weights for routing in dense integrated circuit designs. By incorporating a lightweight LSTM and additional router features, the policy retains sequence context and improves convergence across various placement densities and guide qualities. Integrated into any cost‑based router with minimal changes, the approach reduces design rule violations by an average of 92% and cuts runtime by 10%.

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