# Can an Actor-Critic Optimization Framework Improve Analog Design?

arXiv: https://arxiv.org/abs/2603.24714 · HTML: https://mlsanity.com/paper/2603.24714/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 33 references that are in the map.
- First posted on arXiv 25 Mar 2026.

## Builds on (most cited first, 19 of 19)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023)
- [Deep reinforcement learning from human preferences](https://mlsanity.com/paper/1706.03741/) (2017)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model](https://mlsanity.com/paper/1911.08265/) (2019)
- [OpenAI GPT-5 System Card](https://mlsanity.com/paper/2601.03267/) (2025)
- [AnalogCoder: Analog Circuit Design via Training-Free Code Generation](https://mlsanity.com/paper/2405.14918/) (2024)
- [ADO-LLM: Analog Design Bayesian Optimization with In-Context Learning of Large Language Models](https://mlsanity.com/paper/2406.18770/) (2024)
- [Needle in the Haystack for Memory Based Large Language Models](https://mlsanity.com/paper/2407.01437/) (2024)
- [AmpAgent: An LLM-based Multi-Agent System for Multi-stage Amplifier Schematic Design from Literature for Process and Performance Porting](https://mlsanity.com/paper/2409.14739/) (2024)
- [LayoutCopilot: An LLM-powered Multi-agent Collaborative Framework for Interactive Analog Layout Design](https://mlsanity.com/paper/2406.18873/) (2024)
- [LEDRO: LLM-Enhanced Design Space Reduction and Optimization for Analog Circuits](https://mlsanity.com/paper/2411.12930/) (2024)
- [LLM-Enhanced Bayesian Optimization for Efficient Analog Layout Constraint Generation](https://mlsanity.com/paper/2406.05250/) (2024)
- [EEsizer: LLM-Based AI Agent for Sizing of Analog and Mixed Signal Circuit](https://mlsanity.com/paper/2509.25510/) (2025)
- [LLM-USO: Large Language Model-based Universal Sizing Optimizer](https://mlsanity.com/paper/2502.02764/) (2025)
- [Can Low-Rank Knowledge Distillation in LLMs be Useful for Microelectronic Reasoning?](https://mlsanity.com/paper/2406.13808/) (2024)

## Built on by (most cited first, 1 of 1)

- [Finding the Needle in a Haystack: Test-Time Analog Circuit Representation Adaptation for Bayesian Optimization](https://mlsanity.com/paper/2608.12687/) (2026)

## Abstract (arXiv)

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. While existing optimizers reduce some of this burden, they largely operate without the kind of judgment designers use when deciding where to search next. This paper presents an actor-critic optimization framework (ACOF) for analog sizing that brings that form of guidance into the loop. Rather than treating optimization as a purely black-box search problem, ACOF separates the roles of proposal and evaluation: an actor suggests promising regions of the design space, while a critic reviews those choices, enforces design legality, and redirects the search when progress is hampered. This structure preserves compatibility with standard simulator-based flows while making the search process more deliberate, stable, and interpretable. Across our test circuits, ACOF improves the top-10 figure of merit by an average of 38.9% over the strongest competing baseline and reduces regret by an average of 24.7%, with peak gains of 70.5% in FoM and 42.2% lower regret on individual circuits. By combining iterative reasoning with simulation-driven search, the framework offers a more transparent path toward automated analog sizing across challenging design spaces.