做硬件验证的可以看看，CHORUS用4B小模型在CVDP-ECov上干翻了671B的DeepSeek-R1，靠的是把多个专家模型合并起来，思路挺巧。
CHORUS是一个面向硬件验证测试激励生成的后训练框架，利用分阶段监督微调产生行为多样的检查点，再通过密集奖励强化学习将其转化为任务级优势互补的专家模型。该框架通过无训练模型合并或进一步后训练整合专家优势，最终将多个专家合并为单个4B模型。CHORUS在CVDP-ECov基准上达到88.0%的Pass@1，比DeepSeek-R1（671B）高出13.5个百分点。
CHORUS: Complementary Experts for High-Coverage Testbench Stimulus Generation
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.