[2604.25191] How Can Reinforcement Learning Achieve Expert-level Placement?

Skip to main content

arXiv is now an independent nonprofit!<br>Learn more<br>&times;

Search arXiv

Press Enter to search &middot; Advanced search

-->

Computer Science > Hardware Architecture

arXiv:2604.25191 (cs)

[Submitted on 28 Apr 2026 (v1), last revised 1 Jun 2026 (this version, v2)]

Title:How Can Reinforcement Learning Achieve Expert-level Placement?

Authors:Ruo-Tong Chen, Ke Xue, Chengrui Gao, Yunqi Shi, Tian Xu, Peng Xie, Siyuan Xu, Mingxuan Yuan, Chao Qian, Zhi-Hua Zhou<br>View a PDF of the paper titled How Can Reinforcement Learning Achieve Expert-level Placement?, by Ruo-Tong Chen and 9 other authors

View PDF<br>HTML (experimental)

Abstract:Chip placement is a critical step in physical design. While reinforcement learning (RL)-based methods have recently emerged, their training primarily focuses on wirelength optimization, and therefore often fail to achieve expert-quality layouts. We identify the reward design as the primary cause for the performance gap with experts, and instead of formalizing intricate processes, we circumvent this by directly learning from expert layouts to derive a reward model. Our approach starts from the final expert layouts to infer step-by-step expert trajectories. Using these trajectories as demonstrations or preferences, we train a model that captures the latent implicit rewards in expert results. Experiments show that our framework can efficiently learn from even a single design and generalize well to unseen cases.

Comments:<br>DAC 2026

Subjects:

Hardware Architecture (cs.AR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

Cite as:<br>arXiv:2604.25191 [cs.AR]

(or<br>arXiv:2604.25191v2 [cs.AR] for this version)

https://doi.org/10.48550/arXiv.2604.25191

Focus to learn more

arXiv-issued DOI via DataCite

Submission history<br>From: Chao Qian [view email]<br>[v1]<br>Tue, 28 Apr 2026 03:55:03 UTC (358 KB)

[v2]<br>Mon, 1 Jun 2026 14:43:02 UTC (358 KB)

Full-text links:<br>Access Paper:

View a PDF of the paper titled How Can Reinforcement Learning Achieve Expert-level Placement?, by Ruo-Tong Chen and 9 other authors<br>View PDF<br>HTML (experimental)<br>TeX Source

view license

Current browse context:

cs.AR

next >

new<br>recent<br>| 2026-04

Change to browse by:

cs<br>cs.AI<br>cs.LG

References & Citations

NASA ADS<br>Google Scholar

Semantic Scholar

export BibTeX citation<br>Loading...

BibTeX formatted citation

&times;

loading...

Data provided by:

Bookmark

Bibliographic Tools

Bibliographic and Citation Tools

Bibliographic Explorer Toggle

Bibliographic Explorer (What is the Explorer?)

Connected Papers Toggle

Connected Papers (What is Connected Papers?)

Litmaps Toggle

Litmaps (What is Litmaps?)

scite.ai Toggle

scite Smart Citations (What are Smart Citations?)

Code, Data, Media

Code, Data and Media Associated with this Article

alphaXiv Toggle

alphaXiv (What is alphaXiv?)

Links to Code Toggle

CatalyzeX Code Finder for Papers (What is CatalyzeX?)

DagsHub Toggle

DagsHub (What is DagsHub?)

GotitPub Toggle

Gotit.pub (What is GotitPub?)

Huggingface Toggle

Hugging Face (What is Huggingface?)

ScienceCast Toggle

ScienceCast (What is ScienceCast?)

Demos

Demos

Replicate Toggle

Replicate (What is Replicate?)

Spaces Toggle

Hugging Face Spaces (What is Spaces?)

Spaces Toggle

TXYZ.AI (What is TXYZ.AI?)

Related Papers

Recommenders and Search Tools

Link to Influence Flower

Influence Flower (What are Influence Flowers?)

Core recommender toggle

CORE Recommender (What is CORE?)

Author

Venue

Institution

Topic

About arXivLabs

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .

Which authors of this paper are endorsers? |<br>Disable MathJax (What is MathJax?)

Major funding support from