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LLM4Cov: Execution-Aware Agentic Learning for High-coverage Testbench Generation
arXiv – CS AI|Hejia Zhang, Zhongming Yu, Chia-Tung Ho, Haoxing Ren, Brucek Khailany, Jishen Zhao||6 views
🤖AI Summary
Researchers have developed LLM4Cov, an offline learning framework that enables AI agents to generate high-coverage hardware verification testbenches without expensive online reinforcement learning. A compact 4B-parameter model achieved 69.2% coverage pass rate, outperforming larger models by demonstrating efficient learning from execution feedback in hardware verification tasks.
Key Takeaways
- →LLM4Cov introduces an offline agent-learning approach that avoids expensive online reinforcement learning for hardware verification.
- →The framework uses execution-validated data curation and policy-aware synthesis to enable scalable learning under execution constraints.
- →A 4B-parameter model achieved 69.2% coverage pass rate, beating its teacher model by 5.3%.
- →The compact model demonstrated competitive performance against models an order of magnitude larger.
- →The research addresses the challenge of learning from slow, expensive industrial simulator feedback in hardware verification.
#llm#ai-agents#hardware-verification#offline-learning#testbench-generation#execution-aware#model-efficiency#reinforcement-learning
Read Original →via arXiv – CS AI
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