@inproceedings{liu-etal-2026-hybridthinker,
title = "{H}ybrid{T}hinker: Efficient Chain-of-Thought Reasoning via Compressed Memory and Transient Thought Steps",
author = "Liu, Xin and
Zhao, Runsong and
Liu, Xinyu and
Ruan, Junhao and
Huang, Pengcheng and
Dong, Shichao and
Xiao, Chunyang and
Wang, Chenglong and
Li, Changliang and
Zhu, JingBo and
Xiao, Tong",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1400/",
pages = "31974--32000",
ISBN = "979-8-89176-459-0",
abstract = "Extended chain-of-thought (CoT) traces improve LLM reasoning but incur substantial computational and memory costs. While existing CoT compression methods mitigate this by condensing thought steps into compact representations via memory tokens and retaining only these representations at inference time, the loss of fine-grained information makes subsequent steps more error-prone. To alleviate this, we propose $\textbf{HybridThinker}$, where in addition to preserving these representations, thought steps are also temporarily retained to provide fine-grained details. However, we observe that naively keeping thought steps accessible to subsequent steps \textit{during training} lets the model bypass memory tokens by retrieving information directly from these steps, leaving the model{'}s ability to compress and retrieve information through memory tokens insufficiently trained. We therefore introduce a hybrid training scheme, in which only some thought steps are directly accessible through attention to subsequent steps, while the other thought steps are masked, forcing the model to use memory tokens for compression and retrieval. Across four reasoning benchmarks, HybridThinker matches Vanilla on Qwen2.5-7B and approaches it on Llama3.1-8B, improving over LightThinker by 5.8 and 2.7 points, respectively, with similar inference time. Ablation studies confirm that both temporary thought-step retention and the hybrid training scheme contribute to these gains."
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<abstract>Extended chain-of-thought (CoT) traces improve LLM reasoning but incur substantial computational and memory costs. While existing CoT compression methods mitigate this by condensing thought steps into compact representations via memory tokens and retaining only these representations at inference time, the loss of fine-grained information makes subsequent steps more error-prone. To alleviate this, we propose HybridThinker, where in addition to preserving these representations, thought steps are also temporarily retained to provide fine-grained details. However, we observe that naively keeping thought steps accessible to subsequent steps during training lets the model bypass memory tokens by retrieving information directly from these steps, leaving the model’s ability to compress and retrieve information through memory tokens insufficiently trained. We therefore introduce a hybrid training scheme, in which only some thought steps are directly accessible through attention to subsequent steps, while the other thought steps are masked, forcing the model to use memory tokens for compression and retrieval. Across four reasoning benchmarks, HybridThinker matches Vanilla on Qwen2.5-7B and approaches it on Llama3.1-8B, improving over LightThinker by 5.8 and 2.7 points, respectively, with similar inference time. Ablation studies confirm that both temporary thought-step retention and the hybrid training scheme contribute to these gains.</abstract>
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%0 Conference Proceedings
%T HybridThinker: Efficient Chain-of-Thought Reasoning via Compressed Memory and Transient Thought Steps
%A Liu, Xin
%A Zhao, Runsong
%A Liu, Xinyu
%A Ruan, Junhao
%A Huang, Pengcheng
%A Dong, Shichao
%A Xiao, Chunyang
%A Wang, Chenglong
%A Li, Changliang
%A Zhu, JingBo
%A Xiao, Tong
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F liu-etal-2026-hybridthinker
%X Extended chain-of-thought (CoT) traces improve LLM reasoning but incur substantial computational and memory costs. While existing CoT compression methods mitigate this by condensing thought steps into compact representations via memory tokens and retaining only these representations at inference time, the loss of fine-grained information makes subsequent steps more error-prone. To alleviate this, we propose HybridThinker, where in addition to preserving these representations, thought steps are also temporarily retained to provide fine-grained details. However, we observe that naively keeping thought steps accessible to subsequent steps during training lets the model bypass memory tokens by retrieving information directly from these steps, leaving the model’s ability to compress and retrieve information through memory tokens insufficiently trained. We therefore introduce a hybrid training scheme, in which only some thought steps are directly accessible through attention to subsequent steps, while the other thought steps are masked, forcing the model to use memory tokens for compression and retrieval. Across four reasoning benchmarks, HybridThinker matches Vanilla on Qwen2.5-7B and approaches it on Llama3.1-8B, improving over LightThinker by 5.8 and 2.7 points, respectively, with similar inference time. Ablation studies confirm that both temporary thought-step retention and the hybrid training scheme contribute to these gains.
%U https://aclanthology.org/2026.emnlp-main.1400/
%P 31974-32000
Markdown (Informal)
[HybridThinker: Efficient Chain-of-Thought Reasoning via Compressed Memory and Transient Thought Steps](https://aclanthology.org/2026.emnlp-main.1400/) (Liu et al., EMNLP 2026)
ACL
- Xin Liu, Runsong Zhao, Xinyu Liu, Junhao Ruan, Pengcheng Huang, Shichao Dong, Chunyang Xiao, Chenglong Wang, Changliang Li, JingBo Zhu, and Tong Xiao. 2026. HybridThinker: Efficient Chain-of-Thought Reasoning via Compressed Memory and Transient Thought Steps. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 31974–32000, Budapest, Hungary. Association for Computational Linguistics.