AI搜索：用多智能体协议蒸馏缩小开源与闭源差距
开源AI搜索模型总追不上闭源，因为闭源模型不公开内部推理过程，传统蒸馏只能模仿表面话术，学不到真本事。研究者提出多智能体协议蒸馏（MAPD）：先让多个AI分工——一个拆问题、一个查资料、一个修失败搜索，把整个探索过程写成结构化的JSON协议（包含任务类型、推理计划、事实依据）。训练时，这个协议只给模型的一个特权分支看，让模型既从协议学推理逻辑，又从结果反馈学优化。在7个问答测试集上，1.7B和4B的小模型平均成功率分别达到39.4%和44.4%，远超其他蒸馏方法。它不是你明天能用上的，但指明了方向：用结构化协议替代黑盒模仿，开源模型也能学到闭源的核心推理能力。
📄 原文摘要(英文)
Agentic search enables large language models to solve knowledge-intensive tasks by interleaving multi-step reasoning with retrieval, yet optimizing this with outcome-based reinforcement learning (RL) provides only sparse supervision. Knowledge distillation can supply denser guidance, and advanced proprietary models with their strong reasoning capabilities are promising teachers. While distilling from proprietary models can densify this supervisory signal, conventional logit-matching is precluded by hidden logits and mismatched tokenizers, whereas raw natural language trajectory imitation transfers superficial stylistic artifacts rather than core reasoning competence. To address the heterogeneous distillation problem and bridge the distribution gap, we propose Multi-Agent Protocol Distillation (MAPD), a joint distillation and RL framework uses a structured, style-normalized protocol as an intermediate representation. An offline multi-agent system (MAS) decomposes each query, retrieves supporting evidence, repairs failed searches, and converts the resulting exploration trace into a JSON protocol containing the task type, reasoning plan, and extractive grounding facts. During training, the protocol is provided only to a privileged branch of the student policy, whose token distributions furnish a dense distillation signal alongside the sparse RL objective. Extensive evaluations across seven QA benchmarks demonstrate that MAPD consistently outperforms competitive distillation and RL, achieving average success rates of 39.4\% on Qwen3-1.7B and 44.4\% on Qwen3-4B. Crucially, the framework generalizes robustly across diverse proprietary teachers while effectively mitigating the student policy from style drift and verbosity degeneration.