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Lighthouse RL: Sample-Efficient Circuit Optimization via Strategic Reset Points

Research Theory & Methods

Merged summary

TL;DR — Lighthouse RL is a sample-efficient reinforcement learning method for analog circuit sizing that resets episodes from previously discovered high-performing configurations ("lighthouses") to steer exploration toward promising regions. It matters as a general, plug-and-play RL enhancement for expensive black-box optimization.

Introduces a "strategic reset" strategy: episodes initialize from high-performing states found during training, which sit closer to target objectives and guide exploration.
Targets two failure modes—poor generalization across performance targets and wasted exploration of unpromising regions.
Reported gains vs. RL and Bayesian optimization baselines: up to 1.72x faster sample efficiency, 100% vs. 0–87% success rate, and 75% vs. 0–50% extrapolation/generalization success.
Evaluated on a 2D benchmark and two analog circuits; the reset strategy is presented as plug-and-play for any RL-based optimizer.

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Lighthouse RL: Sample-Efficient Circuit Optimization via Strategic Reset Points

arXiv cs.LG Mustafa Emre Gürsoy, Stefan Uhlich, Ryoga Matsuo, Yağız Gençer, Arun Venkitaraman, Chia-Yu Hsieh, Andrea Bonetti, Eisaku Ohbuchi, Lorenzo Servadei 2026-07-15 arXiv:2607.14008

Introduces a "strategic reset" strategy: episodes initialize from high-performing states found during training, which sit closer to target objectives and guide exploration.
Targets two failure modes—poor generalization across performance targets and wasted exploration of unpromising regions.
Reported gains vs. RL and Bayesian optimization baselines: up to 1.72x faster sample efficiency, 100% vs. 0–87% success rate, and 75% vs. 0–50% extrapolation/generalization success.
Evaluated on a 2D benchmark and two analog circuits; the reset strategy is presented as plug-and-play for any RL-based optimizer.

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