arXiv AI September 7, 2026 By Qingyun Zou, Feng Yu, Hongshi Tan, Yao Chen, Bingsheng He, WengFai Wong

HLS-Seek: QoR-Aware Code Generation for High-Level Synthesis via Proxy Comparative Reward Reinforcement Learning

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.

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