![[2602.19000] MagicAgent: Towards Generalized Agent Planning](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.19000] MagicAgent: Towards Generalized Agent Planning
Summary
The paper presents MagicAgent, a series of foundation models aimed at improving generalized agent planning in AI, addressing challenges in multi-task training and data scarcity.
Why It Matters
As AI evolves, the ability to generalize across various planning tasks is crucial for developing more autonomous and intelligent systems. MagicAgent's innovative approach to synthetic data generation and training paradigms could significantly enhance AI's planning capabilities, impacting fields like robotics and human-computer interaction.
Key Takeaways
- MagicAgent introduces a scalable synthetic data framework for diverse planning tasks.
- A two-stage training process combines supervised fine-tuning with multi-objective reinforcement learning.
- Empirical results show MagicAgent models outperform existing sub-100B models in various benchmarks.
Computer Science > Artificial Intelligence arXiv:2602.19000 (cs) [Submitted on 22 Feb 2026] Title:MagicAgent: Towards Generalized Agent Planning Authors:Xuhui Ren, Shaokang Dong, Chen Yang, Qing Gao, Yunbin Zhao, Yongsheng Liu, Xinwei Geng, Xiang Li, Demei Yan, Yanqing Li, Chenhao Huang, Dingwei Zhu, Junjie Ye, Boxuan Yue, Yingnan Fu, Mengzhe Lv, Zezeng Feng, Boshen Zhou, Bocheng Wang, Xuanjing Huang, Yu-Gang Jiang, Tao Gui, Qi Zhang, Yunke Zhang View a PDF of the paper titled MagicAgent: Towards Generalized Agent Planning, by Xuhui Ren and 23 other authors View PDF HTML (experimental) Abstract:The evolution of Large Language Models (LLMs) from passive text processors to autonomous agents has established planning as a core component of modern intelligence. However, achieving generalized planning remains elusive, not only by the scarcity of high-quality interaction data but also by inherent conflicts across heterogeneous planning tasks. These challenges result in models that excel at isolated tasks yet struggle to generalize, while existing multi-task training attempts suffer from gradient interference. In this paper, we present \textbf{MagicAgent}, a series of foundation models specifically designed for generalized agent planning. We introduce a lightweight and scalable synthetic data framework that generates high-quality trajectories across diverse planning tasks, including hierarchical task decomposition, tool-augmented planning, multi-constraint scheduling, procedural l...
