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[2602.22543] Ruyi2 Technical Report
Summary
The Ruyi2 Technical Report presents advancements in adaptive computing strategies for Large Language Models (LLMs), focusing on efficiency and performance improvements through a new 'Familial Model' architecture.
Why It Matters
This report addresses critical challenges in deploying LLMs, such as cost and latency. By introducing Ruyi2, it offers a novel approach to optimize model performance while maintaining efficiency, which is essential for scalable AI applications.
Key Takeaways
- Ruyi2 enhances adaptive computing strategies for LLMs.
- It achieves 2-3 times speedup over previous models using 3D parallel training.
- The 'Familial Model' architecture allows for efficient parameter sharing.
- Ruyi2 establishes a 'Train Once, Deploy Many' paradigm for AI models.
- The report provides insights into balancing efficiency with high-performance capabilities.
Computer Science > Computation and Language arXiv:2602.22543 (cs) [Submitted on 26 Feb 2026] Title:Ruyi2 Technical Report Authors:Huan Song, Shuyu Tian, Junyi Hao, Minxiu Xu, Hongjun An, Yiliang Song, Jiawei Shao, Xuelong Li View a PDF of the paper titled Ruyi2 Technical Report, by Huan Song and 7 other authors View PDF Abstract:Large Language Models (LLMs) face significant challenges regarding deployment costs and latency, necessitating adaptive computing strategies. Building upon the AI Flow framework, we introduce Ruyi2 as an evolution of our adaptive model series designed for efficient variable-depth computation. While early-exit architectures offer a viable efficiency-performance balance, the Ruyi model and existing methods often struggle with optimization complexity and compatibility with large-scale distributed training. To bridge this gap, Ruyi2 introduces a stable "Familial Model" based on Megatron-LM. By using 3D parallel training, it achieves a 2-3 times speedup over Ruyi, while performing comparably to same-sized Qwen3 models. These results confirm that family-based parameter sharing is a highly effective strategy, establishing a new "Train Once, Deploy Many" paradigm and providing a key reference for balancing architectural efficiency with high-performance capabilities. Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI) Cite as: arXiv:2602.22543 [cs.CL] (or arXiv:2602.22543v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv...
