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🧠 AI🟢 BullishImportance 7/10
MagicAgent: Towards Generalized Agent Planning
arXiv – CS AI|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||3 views
🤖AI Summary
Researchers have developed MagicAgent, a series of foundation models designed for generalized AI agent planning that outperforms existing sub-100B models and even surpasses leading ultra-scale models like GPT-5.2. The models achieve superior performance through a novel synthetic data framework and two-stage training paradigm that addresses gradient interference in multi-task learning.
Key Takeaways
- →MagicAgent introduces a lightweight synthetic data framework for generating high-quality trajectories across diverse planning tasks including hierarchical decomposition and tool execution.
- →The models employ a two-stage training paradigm with supervised fine-tuning followed by multi-objective reinforcement learning to mitigate training conflicts.
- →MagicAgent-32B and MagicAgent-30B-A3B achieve 75.1% on Worfbench and 86.9% on BFCL-v3 benchmarks, outperforming much larger models.
- →The research addresses the core challenge of generalized planning in AI agents, moving beyond isolated task performance.
- →MagicAgent surpasses leading ultra-scale models including GPT-5.2, Kimi-K2, and GLM-4.7 despite being significantly smaller.
#ai-agents#llm#planning#foundation-models#reinforcement-learning#synthetic-data#benchmark#generalization#multi-task-learning
Read Original →via arXiv – CS AI
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