MultiAgent-OPD for End-to-End Hierarchical Multi-Agent Training
October 19, 2026
MultiAgent-OPD employs on-policy distillation and reinforcement learning to jointly train Leader-Searcher-Answerer teams. It uses a frozen Teacher to provide local process supervision for Searcher tokens, overcoming the indirect feedback bottleneck in hierarchical systems.
HOW THIS AFFECTS YOU
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builderYou can use on-policy distillation to train complex multi-agent workflows where only final answers are easily verifiable.
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researcherThis method provides a way to bridge the gap between global rewards and local token-level supervision.