Universal YOCO for Efficient Depth Scaling
The rise of test-time scaling has remarkably boosted the reasoning and agentic proficiency of Large Language Models (LLMs). Yet, standard Transformers struggle to scale inference-time compute efficiently, as conventional looping strategies suffer from high computational overhead and a KV cache that inflates alongside model depth. We present Universal YOCO (YOCO-U), which combines the YOCO decoder-decoder architecture with recursive computation to achieve a synergistic effect greater than either alone. Built on the YOCO framework, YOCO-U implements a Universal Self-Decoder that performs multiple iterations via parameter sharing, while confining the iterative process to shallow, efficient-attention layers. This combination yields a favorable capability-efficiency tradeoff that neither YOCO nor recursion achieves independently. The YOCO architecture provides a constant global KV cache and linear pre-filling, while partial recursion enhances representational depth with limited overhead. Together, YOCO-U improves token utility and scaling behavior while maintaining efficient inference. Empirical results confirm that YOCO-U remains highly competitive in general and long-context benchmarks, demonstrating that the integration of efficient-attention architectures and recursive computation is a promising direction for scalable LLMs.
69cdd86dacf91530b21df95c
Yutao Sun
69cdd86dacf91530b21df95d
Li Dong
claimed_verified
2026-04-02T08:13:18.335Z
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/avatars/2331cf703c1b5d3a62e2050b1a6eb108.svg
Li Dong
unilm
user
unilm
69cdd86dacf91530b21df95e
Tianzhu Ye
69cdd86dacf91530b21df95f
Shaohan Huang
69cdd86dacf91530b21df960
Jianyong Wang
69cdd86dacf91530b21df961
Furu Wei
Universal YOCO for Efficient Depth Scaling
The rise of test-time scaling has remarkably boosted the reasoning and agentic proficiency of Large Language Models (LLMs). Yet, standard Transformers struggle to scale inference-time compute efficiently, as conventional looping strategies suffer from high computational overhead and a KV cache that inflates alongside model depth. We present Universal YOCO (YOCO-U), which combines the YOCO decoder-decoder architecture with recursive computation to achieve a synergistic effect greater than either alone. Built on the YOCO framework, YOCO-U implements a Universal Self-Decoder that performs multiple iterations via parameter sharing, while confining the iterative process to shallow, efficient-attention layers. This combination yields a favorable capability-efficiency tradeoff that neither YOCO nor recursion achieves independently. The YOCO architecture provides a constant global KV cache and linear pre-filling, while partial recursion enhances representational depth with limited overhead. Together, YOCO-U improves token utility and scaling behavior while maintaining efficient inference. Empirical results confirm that YOCO-U remains highly competitive in general and long-context benchmarks, demonstrating that the integration of efficient-attention architectures and recursive computation is a promising direction for scalable LLMs.
Universal YOCO combines YOCO decoder-decoder architecture with recursive computation to achieve efficient inference and improved scaling behavior in large language models.
Large Language Models Transformers YOCO decoder-decoder architecture recursive computation parameter sharing attention layers KV cache pre-filling token utility scaling behavior