# CARVE: Breaking Data Barriers in Chip Placement by Harnessing Reusable Expertise

arXiv: https://arxiv.org/abs/2609.33106 · HTML: https://mlsanity.com/paper/2609.33106/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 26 references that are in the map.
- First posted on arXiv 27 Sep 2026.

## Builds on (most cited first, 16 of 16)

- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Gradient Episodic Memory for Continual Learning](https://mlsanity.com/paper/1706.08840/) (2017)
- [Progressive Neural Networks](https://mlsanity.com/paper/1606.04671/) (2016)
- [PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning](https://mlsanity.com/paper/1711.05769/) (2017)
- [Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning](https://mlsanity.com/paper/1809.08835/) (2018)
- [Understanding plasticity in neural networks](https://mlsanity.com/paper/2303.01486/) (2023)
- [Continual World: A Robotic Benchmark For Continual Reinforcement Learning](https://mlsanity.com/paper/2105.10919/) (2021)
- [Disentangling the Causes of Plasticity Loss in Neural Networks](https://mlsanity.com/paper/2402.18762/) (2024)
- [ChiPFormer: Transferable Chip Placement via Offline Decision Transformer](https://mlsanity.com/paper/2306.14744/) (2023)
- [CircuitNet: An Open-Source Dataset for Machine Learning Applications in Electronic Design Automation (EDA)](https://mlsanity.com/paper/2208.01040/) (2022)
- [An Updated Assessment of Reinforcement Learning for Macro Placement](https://mlsanity.com/paper/2302.11014/) (2023)
- [SICNav-Diffusion: Safe and Interactive Crowd Navigation with Diffusion Trajectory Predictions](https://mlsanity.com/paper/2503.08858/) (2025)
- [Self-Composing Policies for Scalable Continual Reinforcement Learning](https://mlsanity.com/paper/2506.14811/) (2025)
- [Chip Placement with Diffusion Models](https://mlsanity.com/paper/2407.12282/) (2024)
- [That Chip Has Sailed: A Critique of Unfounded Skepticism Around AI for Chip Design](https://mlsanity.com/paper/2411.10053/) (2024)
- [Don't Freeze, Don't Crash: Extending the Safe Operating Range of Neural Navigation in Dense Crowds](https://mlsanity.com/paper/2603.06729/) (2026)

## Abstract (arXiv)

Pretrained macro-placement policies can reduce repeated optimization across circuits, but deployment often exposes them to unfamiliar designs when the original training data are unavailable. Repeatedly fine-tuning a single serving model can overwrite earlier improvements, while simply saving checkpoints does not determine where they can be reliably reused. We introduce Continual Adaptation through the Reuse of Validated Expertise (CARVE), a framework that represents accumulated expertise as a frozen base policy, immutable specialists, and task-specific credentials obtained through local validation. For a new task, CARVE first checks existing specialists and trains a new specialist from the frozen base only when none qualifies. Under fixed task distributions and validation rules that control cumulative error, we establish expected-performance guarantees for repeated reuse. For bounded losses, we also derive matching worst-case bounds on the local samples needed for reliable reuse. In macro placement, a reuse-first follow-up reduces recorded training time by 58.5% (9.66 to 4.01 hours), while mean HPWL gain changes only from 8.41% to 7.86%. In a simulated receiving deployment, imported specialists are reused on six of seven new IBM circuits with no receiver-side training, achieving a 5.76% mean HPWL gain. Navigation studies provide complementary evidence on repair retention and repeated adaptation.