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[2511.12033] EARL: Entropy-Aware RL Alignment of LLMs for Reliable RTL Code Generation
Summary
The paper presents EARL, an Entropy-Aware Reinforcement Learning framework designed to enhance the reliability of RTL code generation by focusing on critical tokens that influence functional correctness.
Why It Matters
As large language models (LLMs) are increasingly used in hardware design automation, ensuring their outputs align with designer intent is crucial. EARL addresses common issues in RTL code generation, such as syntax errors and functional hallucinations, by optimizing learning processes, which can lead to more reliable hardware design tools.
Key Takeaways
- EARL improves RTL code generation by focusing on high-entropy tokens.
- The framework uses reinforcement learning with verifiable rewards for better alignment with design intent.
- Experiments show up to 14.7% improvement in functional pass rates compared to previous models.
- Entropy analysis helps identify critical tokens that significantly impact code correctness.
- The approach enhances training stability by reducing unnecessary updates.
Computer Science > Machine Learning arXiv:2511.12033 (cs) [Submitted on 15 Nov 2025 (v1), last revised 24 Feb 2026 (this version, v2)] Title:EARL: Entropy-Aware RL Alignment of LLMs for Reliable RTL Code Generation Authors:Jiahe Shi, Zhengqi Gao, Ching-Yun Ko, Duane Boning View a PDF of the paper titled EARL: Entropy-Aware RL Alignment of LLMs for Reliable RTL Code Generation, by Jiahe Shi and 3 other authors View PDF HTML (experimental) Abstract:Recent advances in large language models (LLMs) have demonstrated significant potential in hardware design automation, particularly in using natural language to synthesize Register-Transfer Level (RTL) code. Despite this progress, a gap remains between model capability and the demands of real-world RTL design, including syntax errors, functional hallucinations, and weak alignment to designer intent. Reinforcement Learning with Verifiable Rewards (RLVR) offers a promising approach to bridge this gap, as hardware provides executable and formally checkable signals that can be used to further align model outputs with design intent. However, in long, structured RTL code sequences, not all tokens contribute equally to functional correctness, and naïvely spreading gradients across all tokens dilutes learning signals. A key insight from our entropy analysis in RTL generation is that only a small fraction of tokens (e.g., always, if, assign, posedge) exhibit high uncertainty and largely influence control flow and module structure. To addre...
