GRADE用17B参数在MMLUPro上以一半计算量超基线4.8分，靠四个门控路由动态调深度，很实用。
GRADE是一种分层多智能体系统，通过四个轻量级门控网络联合控制智能体选择、层次深度、智能体间通信和分支修剪。训练采用CoGRPO（协作组相对策略优化），无需批评网络，为参与rollout的每个门控和智能体分配共享优势信号。在平均约17B活跃参数下，GRADE在GSM8K、MMLUPro和GPQA上超越所有基线，其中在MMLUPro上以一半活跃计算量高出最强基线4.8个百分点。在AIME-2025上，GRADE与现有框架竞争力持平。消融实验表明层次结构和掩码交叉注意力是精度最大贡献者，且每个智能体的校准对安全热替换必要。
Route, Communicate, and Reason: Gated Routing and Adaptive Depth for Efficient Multi-Agent Reasoning
Multi-agent ensembling multiplies active parameters and inference cost without answering three basic questions: which agents to consult, how deeply a query should traverse a hierarchy of agents, and when inter-agent communication is worth its cost. We present GRADE (Gated Routing and Adaptive Depth for Efficient Reasoning), a hierarchical multi-agent system in which four lightweight learned gates jointly govern agent selection, hierarchy depth, inter-agent communication, and branch pruning. Training uses CoGRPO (Collaborative Group-Relative Policy Optimization), a novel critic-free recipe that adapts GRPO to multi-agent hierarchies and assigns a shared advantage signal to every gate and agent that participated in a rollout. Agent models are drawn from a hot-swappable Expert Registry; per-agent calibration maps allow experts to be replaced at inference time without retraining. At $\sim$17B average active parameters, GRADE outperforms all baselines on GSM8K, MMLUPro, and GPQA, surpassing the strongest baseline by 4.8 points on MMLUPro at half the active compute. On AIME-2025, where model depth dominates, GRADE remains competitive to existing frameworks. Ablations isolate the hierarchy and masked cross-attention as the largest contributors to accuracy, and show that per-agent calibration is necessary for safe hot-swapping.