@inproceedings{chen-etal-2026-optico,
    title = "{O}pti{C}o: Adaptive Distributed Training Optimization via Collaborative Agent Reasoning",
    author = "Chen, Sheng  and
      Zhe, Tang  and
      Zhang, Weixing  and
      Yang, Fei  and
      Wang, Yuanyuan.  and
      Li, Tianlin  and
      Liu, Yang",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.1283/",
    pages = "27847--27867",
    ISBN = "979-8-89176-390-6",
    abstract = "Optimizing distributed training strategies for large-scale deep learning models remains a critical challenge in both industry and academia, demanding extensive domain expertise and manual tuning. Existing automated distributed training frameworks are plagued by over-reliance on prior profiling, poor generalization across models/hardware, and scalability constraints stemming from vast search spaces, impeding real-world applicability. To address these challenges, we propose OptiCo, a model-driven multi-agent framework that leverages Large Language Models (LLMs) to enable automatic and explainable distributed training strategy configuration. OptiCo orchestrates a team of reasoning-driven agents, through a shared Global Message Pool facilitating persistent memory and coordination. By employing inception prompting and Chain-Of-Thought (COT) reasoning, agents iteratively refine configurations, detect bottlenecks, analyze failures, and optimize resource utilization. Evaluated across 25+ configurations spanning diverse model architectures, GPU types and scales, OptiCo outperforms expert-designed strategies within 20 iterations, achieving an average performance improvement of 1.84{\%}, with gains ranging from 0.08{\%} to 8.65{\%}. The source codes are avaiable at https://github.com/TangZhe96/OptiCo-public."
}