The paper introduces a reinforcement learning framework that uses large language models to generate RTL circuits while optimizing for Pareto-optimality across power, performance, and area (PPA). It proposes a generation-based training pipeline and a Hypervolume Contribution reward to improve both the quality and diversity of the entire set of generated circuits. Experiments on the RTLLM v2 benchmark show significant gains in hypervolume and diversity compared to baseline and single-objective methods.
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.
arXiv:2507. 04136v2 Announce Type: replace Abstract: This survey offers a comprehensive foundation on the integration of RL with language models, highlighting prominent algorithms such as Proximal Policy Optimization (PPO), Q-Learning, and Actor-Critic methods.
arXiv:2607. 06175v1 Announce Type: cross Abstract: Large language models (LLMs) can generate BPMN process models from natural-language descriptions, yet supervised fine-tuning (SFT) limits their output quality to the patterns present in the training data.
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.
HLS-Seek is a natural‑language‑to‑High‑Level‑Synthesis framework that optimizes Quality of Results (QoR) such as latency and resource usage by using a comparative proxy reward model instead of full synthesis‑in‑the‑loop reinforcement learning. The system achieves high syntax and functional correctness (84.7% and 81.4% respectively) with only 7 B parameters, surpasses GPT‑5.1 on functional pass@5, and trains 8.5× faster than real‑reward RL. In QoR evaluation, HLS‑Seek attains the lowest latency on 19 of 30 kernels and Pareto‑dominates baseline HLS tools on 9 kernels.
arXiv:2606. 05253v1 Announce Type: new Abstract: Large language models (LLMs) have shown increasing promise in generating functionally correct register-transfer-level (RTL) hardware designs.
arXiv:2603. 13319v2 Announce Type: replace Abstract: Diffusion Large Language Models (dLLMs) have emerged as a promising paradigm for parallel token generation, with block-wise variants garnering significant research interest.
arXiv:2607. 19044v1 Announce Type: new Abstract: Leveraging large language models (LLMs) for molecular generation has shown remarkable potential in chemical and drug design.
arXiv:2610.03423v1 Announce Type: new Abstract: Reinforcement learning (RL) for Text-to-3D (T23D) generation requires optimization across multiple quality dimensions such as semantic alignment and te...
arXiv:2608. 01804v1 Announce Type: new Abstract: Post-training large language models (LLMs) via reinforcement learning (RL) has significantly advanced code generation capabilities.
arXiv:2605.28360v3 Announce Type: replace Abstract: Automatic prompt optimization (APO) has driven significant gains in LLM-based agentic workflows. However, most existing methods treat each task's p...
arXiv:2603. 09161v2 Announce Type: replace-cross Abstract: Learning effective netlist representations is fundamentally constrained by the scarcity of labeled datasets, as real designs are protected by Intellectual Property (IP) and costly to annotate.