arXiv:2608. 04999v1 Announce Type: cross Abstract: Analog circuit design automation using reinforcement learning (RL) has emerged as a promising approach for reducing manual effort.
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arXiv:2607. 14008v1 Announce Type: new Abstract: In this paper, we introduce Lighthouse RL, a sample-efficient reinforcement learning (RL) approach for analog circuit sizing.
arXiv:2603. 24714v2 Announce Type: replace Abstract: Analog design often slows down because even small changes to device sizes or biases require expensive simulation cycles, and high-quality solutions typically occupy only a narrow part of a very large search space.
arXiv:2608. 13472v1 Announce Type: cross Abstract: Analog circuit design is a time-consuming, iterative process in a nonlinear and high-dimensional design space that relies heavily on expert intuition.
arXiv:2607. 04033v1 Announce Type: cross Abstract: Optimizer selection for large-scale model training has become a system-level design decision constrained jointly by compute, memory, tuning budget, and task diversity, yet the landscape of over one hundred methods remains fragmented.
arXiv:2608. 16072v1 Announce Type: cross Abstract: Reinforcement learning (RL) with group-relative advantages has become the de facto standard for post-training language model reasoners.
arXiv:2607. 27953v1 Announce Type: new Abstract: Combinatorial optimization problems (COPs) underpin many real-world decisions, but their exponentially large search spaces make high-quality solutions costly to obtain.