Decoder-Hybrid-Decoder Architecture for Efficient Reasoning with Long
  Generation
Authors: name: Liliang Ren, name: Congcong Chen, name: Haoran Xu, name: Young Jin Kim, name: Adam Atkinson, name: Zheng Zhan, name: Jiankai Sun, name: Baolin Peng, name: Liyuan Liu, name: Shuohang Wang, name: Hao Cheng, name: Jianfeng Gao, name: Weizhu Chen, name: Yelong Shen

Recent advances in language modeling have demonstrated the effectiveness of
State Space Models (SSMs) for efficient sequence modeling. While hybrid
architectures such as Samba and the decoder-decoder architecture, YOCO, have
shown promising performance gains over Transformers, prior works have not
investigated the efficiency potential of representation sharing between SSM
layers. In this paper, we introduce the Gated Memory Unit (GMU), a simple yet
effective mechanism for efficient memory sharing across layers. We apply it to
create SambaY, a decoder-hybrid-decoder architecture that incorporates GMUs in
the cross-decoder to share memory readout states from a Samba-based
self-decoder. SambaY significantly enhances decoding efficiency, preserves
linear pre-filling time complexity, and boosts long-context performance, all
while eliminating the need for explicit positional encoding. Through extensive
scaling experiments, we demonstrate that our model exhibits a significantly
lower irreducible loss compared to a strong YOCO baseline, indicating superior
performance scalability under large-scale compute regimes. Our largest model
enhanced with Differential Attention, Phi4-mini-Flash-Reasoning, achieves
significantly better performance than Phi4-mini-Reasoning on reasoning tasks
such as Math500, AIME24/25, and GPQA Diamond without any reinforcement
learning, while delivering up to 10x higher decoding throughput on 2K-length
prompts with 32K generation length under the vLLM inference framework. We
release our training codebase on open-source data at
https://github.com/microsoft/ArchScale.