arXiv AI June 2, 2026 By Ruo-Tong Chen, Ke Xue, Chengrui Gao, Yunqi Shi, Tian Xu, Peng Xie, Siyuan Xu, Mingxuan Yuan, Chao Qian, Zhi-Hua Zhou

How Can Reinforcement Learning Achieve Expert-level Placement?

Read the original on arXiv AI →

arXiv:2604. 25191v2 Announce Type: replace-cross Abstract: Chip placement is a critical step in physical design.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

reinforcement-learning

arXiv AI

Jun 2

ExpertGen: Scalable Sim-to-Real Expert Policy Learning from Imperfect Behavior Priors

arXiv:2603. 15956v3 Announce Type: replace-cross Abstract: Learning generalizable and robust behavior cloning policies requires large volumes of high-quality robotics data.

By Zifan Xu, Ran Gong, Maria Vittoria Minniti, Kausik Sivakumar, Ahmet Salih Gundogdu, Eric Rosen, Riedana Yan, Tushar Kusnur, Zixing Wang, Di Deng, Peter Stone, Xiaohan Zhang, Karl Schmeckpeper

llms diffusion reinforcement-learning robotics benchmarks

arXiv AI

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arXiv:2506. 06793v2 Announce Type: replace-cross Abstract: Reward assignment from scarce demonstrations is a key challenge in both offline and online imitation learning.

By Zixuan Dong, Yumi Omori, Keith Ross

reinforcement-learning benchmarks safety

arXiv Machine Learning

Jun 2

Coherent Off-Policy Improvement of Large Behavior Models with Learned Rewards

arXiv:2606. 02194v1 Announce Type: new Abstract: Distilling expert demonstration data into large generative models using behavioral cloning is a scalable approach to learning capable policies for robotic control, particularly for dexterous manipulation.

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arXiv AI

Jul 2

Selective Expert Guidance for Effective and Diverse Exploration in Reinforcement Learning of LLMs

arXiv:2510. 04140v2 Announce Type: replace Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has become a widely adopted technique for enhancing the reasoning ability of Large Language Models (LLMs).

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llms reinforcement-learning

arXiv Machine Learning

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Variational Proximal Policy Optimization

arXiv:2606. 08032v1 Announce Type: cross Abstract: Reinforcement Learning from Human Feedback via Proximal Policy Optimization often suffers from policy mode collapse, brittle exploration loops, and distribution drift.

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arXiv AI

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Simulation-Aware In-Context Policy Improvement for LLM-Aided Analog Layout Refinement

arXiv:2608. 13767v1 Announce Type: new Abstract: Analog IC layout design remains a labor-intensive iterative process dominated by simulation-driven refinement.

By Bingyang Liu, Ziming Wei, Xiaohan Gao, David Z. Pan

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