Alibaba's MultiAgent-OPD trains hierarchical multi-agent systems end-to-end with on-policy distillation
TaoLiveAIGC · hf · 2026-10-26
Alibaba Group (TaoLive-AIGC team) with Zhejiang University published MultiAgent-OPD, an end-to-end training method for hierarchical multi-agent systems.
- Challenge: In a hierarchical setup a LEADER assigns tasks and subagents execute them, but final answers only give indirect feedback to subagents; LLM-judge scores depend on the judge's own assessment.
- Method: Jointly train the whole agent team via on-policy distillation plus reinforcement learning, using a frozen teacher model for local process supervision.
- Improves end-to-end performance across multiple datasets. On arXiv Oct 20, 2026.
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