Ruyi2 Technical Report
#Large Language Models #Ruyi2 #Adaptive Computing #Familial Model #Megatron-LM #3D Parallel Training #Train Once Deploy Many #Model Optimization
📌 Key Takeaways
- Ruyi2 achieves 2-3 times speedup over previous Ruyi model through 3D parallel training
- The model introduces a stable 'Familial Model' based on Megatron-LM
- Performance comparable to same-sized Qwen3 models while being more efficient
- Establishes a new 'Train Once, Deploy Many' paradigm for LLM deployment
📖 Full Retelling
A team of eight researchers led by Huan Song introduced Ruyi2, an advanced adaptive Large Language Model framework, in a technical report submitted to arXiv on February 26, 2026, addressing significant challenges in deployment costs and latency through a novel 'Familial Model' approach based on Megatron-LM that achieves 2-3 times speedup over previous iterations. The Ruyi2 framework represents a significant evolution in adaptive computing strategies for Large Language Models, building upon the AI Flow framework to introduce a stable architecture that effectively addresses optimization complexity and compatibility issues with large-scale distributed training that plagued earlier approaches. By implementing 3D parallel training techniques, the researchers achieved substantial performance improvements, making Ruyi2 not only faster than its predecessor but also competitive with established models like Qwen3 of similar size. The technical report highlights how family-based parameter sharing serves as a highly effective strategy, establishing a new 'Train Once, Deploy Many' paradigm in the field of artificial intelligence that offers a key reference for balancing architectural efficiency with high-performance capabilities.
🏷️ Themes
Adaptive Computing, Model Efficiency, Distributed Training
📚 Related People & Topics
Large language model
Type of machine learning model
A large language model (LLM) is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation. The largest and most capable LLMs are generative pre-trained transformers (GPTs) that provide the c...
Entity Intersection Graph
Connections for Large language model:
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Artificial intelligence
3 shared
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Reinforcement learning
3 shared
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Educational technology
2 shared
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Benchmark
2 shared
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OpenAI
2 shared
Mentioned Entities
Original Source
--> Computer Science > Computation and Language arXiv:2602.22543 [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 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.2602.22543 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Jiawei Shao [ view email ] [v1] Thu, 26 Feb 2026 02:34:49 UTC (1,312 KB) Full-text links: Access Paper: View a PDF of the paper titled Ruyi2 Technical Report, by Huan Song and 7 other authors View PDF TeX Source view license Current browse context: cs.CL < prev | next > new | recent | 2026-02 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar export...
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