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RTL-Interact is a multi-task post-training framework that trains a single RTL-specialized LLM to generate RTL, construct EDA checks, and revise designs using tool feedback, paired with Pattern-Aware Policy Optimization (PAPO) for multi-task RL. On CVDP, RTL-Interact-32B reaches 48.7% weighted Pass@1 and 68.9% Pass@5 (surpassing DeepSeek-V3.2), while PAPO cuts average tool calls by 63.8% and the models outperform RTL-specialized baselines on VerilogEval v2 and RTLLM v2.
TicTacBench is a new benchmark for evaluating coding agents' timing closure capabilities in RTL design, revealing that current agents have significant room for improvement and proposing TicTacSkill to enhance performance.