OpenAI 推出的 RGA-Designer，能让多智能体系统在保持性能的同时，减少 20.5% 的 token 消耗，比之前的 ARG-Designer 更高效。
RGA-Designer 是一种新的图生成模型，用于设计多智能体系统的通信拓扑。该模型通过引入奖励模型来指导生成过程，奖励模型同时考虑任务正确性和结构紧凑性。在保持与 ARG-Designer 相同任务准确率的同时，RGA-Designer 将平均令牌消耗降低了 20.5%。
Reward-Guided Autoregressive Graph Generation for Efficient Multi-Agent Communication Topology Design
LLM-based Multi-Agent Systems (MAS) achieve strong performance on complex reasoning tasks by coordinating multiple agents, but at the cost of substantial token consumption. Recent work on automatic topology design, ARG-Designer, has reframed this problem as autoregressive graph generation. However, its training objective provides no explicit incentive for the model to generate sparse and efficient topologies. We address this limitation by introducing a Reward-Guided Autoregressive Graph Generation (RGA-Designer) inspired by Reinforcement Learning from Human Feedback (RLHF). We train a reward model that jointly captures task correctness and structural compactness, and then fine-tune the pretrained graph generator using the reward model as feedback. Our method preserves task accuracy at the level of ARG-Designer while reducing token consumption by an average of 20.5%.