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From Proprietary to Open-Source: Bridging the Distribution Gap via Multi-Agent Protocol Distillation in Agentic Search

64 upvotes · 27 JUL 2026 · Junlin Liu, Jiangwang Chen, Zixin Song et al.

This paper proposes a new method to improve the performance of large language models on knowledge-intensive tasks by distilling knowledge from proprietary models and using reinforcement learning. Practitioners may care about this approach because it can help bridge the gap between proprietary and open-source models, leading to more effective and robust AI systems.

agentic searchknowledge distillationmulti-agent systemsreinforcement learningopen-source models