Researchers propose Multi-Objective RL with LLMs for Pareto-Optimal RTL code generation
A new study introduces a reinforcement‑learning framework that guides large language models to produce register‑transfer‑level (RTL) circuits optimized for multiple objectives simultaneously. The approach treats the quality and diversity of an entire set of generated circuits as the optimization target, using a reward based on Hypervolume Contribution (HVC) to measure each circuit’s impact on…
Key points
- New method uses reinforcement learning with hypervolume contribution reward to improve Pareto front coverage.
- Achieves +13.3 pp relative hypervolume over baseline without PPA optimization on RTLLM v2 benchmark.
- Beats single‑objective RL by +14.4 pp and shows higher circuit diversity.
Experiments on the RTLLM v2 benchmark show the method raises relative hypervolume by 13.3 percentage points compared with a baseline that lacks PPA (Power, Performance, Area) optimization, and by 14.4 points over a single‑objective reinforcement‑learning baseline. In addition to higher hypervolume, the technique produces a more diverse collection of designs, giving hardware designers a broader choice of trade‑offs after generation.
The authors claim this is the first learning method that optimizes the set‑level quality of LLM‑generated RTL code rather than focusing on a single circuit, addressing practical design scenarios where product requirements shift among power, latency and area constraints.
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