Hybrid Linear Attention Done Right: Efficient Distillation and Effective Architectures for Extremely Long Contexts
Hybrid Transformer architectures, which combine softmax attention blocks and recurrent neural networks (RNNs), have shown a desirable performance-throughput tradeoff for long-context modeling, but their adoption and studies are hindered by the prohibitive cost of large-scale pre-training from scratch. Some recent studies have shown that pre-trained softmax attention blocks can be converted into RNN blocks through parameter transfer and knowledge distillation. However, these transfer methods require substantial amounts of training data (more than 10B tokens), and the resulting hybrid models also exhibit poor long-context performance, which is the scenario where hybrid models enjoy significant inference speedups over Transformer-based models. In this paper, we present HALO (Hybrid Attention via Layer Optimization), a pipeline for distilling Transformer models into RNN-attention hybrid models. We then present HypeNet, a hybrid architecture with superior length generalization enabled by a novel position encoding scheme (named HyPE) and various architectural modifications. We convert the Qwen3 series into HypeNet using HALO, achieving performance comparable to the original Transformer models while enjoying superior long-context performance and efficiency. The conversion requires just 2.3B tokens, less than 0.01% of their pre-training data
697c6643a67238fac88cc257
Yingfa Chen
claimed_verified
2026-01-30T09:35:16.767Z
6144e4667f2544bb450787b2
https://cdn-avatars.huggingface.co/v1/production/uploads/6144e4667f2544bb450787b2/5wSDDqJbI4TGtBLP9IvjY.png
Yingfa Chen
chen-yingfa
user
chen-yingfa
697c6643a67238fac88cc258
Zhen Leng Thai
697c6643a67238fac88cc259
Zihan Zhou
697c6643a67238fac88cc25a
Zhu Zhang
697c6643a67238fac88cc25b
Xingyu Shen
697c6643a67238fac88cc25c
Shuo Wang
697c6643a67238fac88cc25d
Chaojun Xiao
697c6643a67238fac88cc25e
Xu Han
697c6643a67238fac88cc25f
Zhiyuan Liu
Hybrid Linear Attention Done Right: Efficient Distillation and Effective Architectures for Extremely Long Contexts
Hybrid Transformer architectures, which combine softmax attention blocks and recurrent neural networks (RNNs), have shown a desirable performance-throughput tradeoff for long-context modeling, but their adoption and studies are hindered by the prohibitive cost of large-scale pre-training from scratch. Some recent studies have shown that pre-trained softmax attention blocks can be converted into RNN blocks through parameter transfer and knowledge distillation. However, these transfer methods require substantial amounts of training data (more than 10B tokens), and the resulting hybrid models also exhibit poor long-context performance, which is the scenario where hybrid models enjoy significant inference speedups over Transformer-based models. In this paper, we present HALO (Hybrid Attention via Layer Optimization), a pipeline for distilling Transformer models into RNN-attention hybrid models. We then present HypeNet, a hybrid architecture with superior length generalization enabled by a novel position encoding scheme (named HyPE) and various architectural modifications. We convert the Qwen3 series into HypeNet using HALO, achieving performance comparable to the original Transformer models while enjoying superior long-context performance and efficiency. The conversion requires just 2.3B tokens, less than 0.01% of their pre-training data
HALO enables efficient conversion of Transformer models to RNN-attention hybrid architectures with improved long-context performance using minimal training data.
Hybrid Transformer architectures softmax attention blocks recurrent neural networks parameter transfer knowledge distillation Transformer models RNN-attention hybrid models HALO HypeNet position encoding HyPE Qwen3 series