AgentArk: Distilling Multi-Agent Intelligence into a Single LLM Agent
While large language model (LLM) multi-agent systems achieve superior reasoning performance through iterative debate, practical deployment is limited by their high computational cost and error propagation. This paper proposes AgentArk, a novel framework to distill multi-agent dynamics into the weights of a single model, effectively transforming explicit test-time interactions into implicit model capabilities. This equips a single agent with the intelligence of multi-agent systems while remaining computationally efficient. Specifically, we investigate three hierarchical distillation strategies across various models, tasks, scaling, and scenarios: reasoning-enhanced fine-tuning; trajectory-based augmentation; and process-aware distillation. By shifting the burden of computation from inference to training, the distilled models preserve the efficiency of one agent while exhibiting strong reasoning and self-correction performance of multiple agents. They further demonstrate enhanced robustness and generalization across diverse reasoning tasks. We hope this work can shed light on future research on efficient and robust multi-agent development. Our code is at https://github.com/AIFrontierLab/AgentArk.
6985136a4ad556f294b7ea61
Yinyi Luo
6985136a4ad556f294b7ea62
Yiqiao Jin
6985136a4ad556f294b7ea63
Weichen Yu
6985136a4ad556f294b7ea64
Mengqi Zhang
6985136a4ad556f294b7ea65
Srijan Kumar
6985136a4ad556f294b7ea66
Xiaoxiao Li
6985136a4ad556f294b7ea67
Weijie Xu
6985136a4ad556f294b7ea68
Xin Chen
6985136a4ad556f294b7ea69
Jindong Wang
AgentArk: Distilling Multi-Agent Intelligence into a Single LLM Agent
While large language model (LLM) multi-agent systems achieve superior reasoning performance through iterative debate, practical deployment is limited by their high computational cost and error propagation. This paper proposes AgentArk, a novel framework to distill multi-agent dynamics into the weights of a single model, effectively transforming explicit test-time interactions into implicit model capabilities. This equips a single agent with the intelligence of multi-agent systems while remaining computationally efficient. Specifically, we investigate three hierarchical distillation strategies across various models, tasks, scaling, and scenarios: reasoning-enhanced fine-tuning; trajectory-based augmentation; and process-aware distillation. By shifting the burden of computation from inference to training, the distilled models preserve the efficiency of one agent while exhibiting strong reasoning and self-correction performance of multiple agents. They further demonstrate enhanced robustness and generalization across diverse reasoning tasks. We hope this work can shed light on future research on efficient and robust multi-agent development. Our code is at https://github.com/AIFrontierLab/AgentArk.
AgentArk distills multi-agent reasoning dynamics into a single model through hierarchical distillation strategies, enabling efficient yet powerful reasoning capabilities.
multi-agent systems large language models distillation reasoning-enhanced fine-tuning trajectory-based augmentation process-aware distillation multi-agent dynamics model distillation