CHORUS: Complementary Experts for High-Coverage Testbench Stimulus Generation

Researchers introduced CHORUS, a post-training framework that combines behaviorally diverse LLM checkpoints into a single 4B model, achieving 88.0% Pass@1 on the CVDP-ECov hardware verification benchmark, outperforming DeepSeek-R1 (671B) by 13.5 percentage points. The framework leverages staged supervised fine-tuning and dense-reward reinforcement learning to create complementary experts, which are then merged or further post-trained to exceed the best individual expert's performance.

arXiv:2608.10090v1 Announce Type: new Abstract: Large language models LLMs have advanced code generation, where executable feedback provides a more reliable learning signal than textual imitation alone. Hardware verification is an important application of code generation and accounts for a substantial fraction of modern chip design effort, with high-coverage testbench stimulus generation as a key task. We present CHORUS, a post-training framework that pushes performance beyond what a conventional supervised fine-tuning SFT -to-reinforcement learning RL pipeline achieves. CHORUS builds on two observations. First, staged SFT produces behaviorally diverse checkpoints, and dense-reward RL turns them into strong experts with comparable aggregate performance but distinct task-level strengths. Second, these complementary strengths can be exploited through either training-free model merging or further post-training to outperform the best individual expert. By consolidating the resulting specialists into a single 4B model, CHORUS achieves 88.0% Pass@1 on CVDP-ECov, outperforming DeepSeek-R1 671B by 13.5 percentage points.