@inproceedings{solgi-etal-2026-lori,
title = "{L}o{R}i: Low-Rank Distillation for Implicit Reasoning",
author = "Solgi, Ryan and
Tian, Jiayi and
Zhang, Zheng",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.2322/",
doi = "10.18653/2026.findings-emnlp.2322",
pages = "49860--49871",
ISBN = "979-8-89176-462-0",
abstract = "Implicit chain-of-thought (iCoT) methods aim to internalize reasoning in large language models, but often underperform explicit CoT prompting. We empirically find that hidden-state reasoning trajectories exhibit low-rank structure. Motivated by this observation, we propose a low-rank distillation framework that transfers reasoning by aligning teacher and student trajectories in a shared low-rank tensor subspace using first- and second-order statistics. The resulting formulation captures the global structure of reasoning while supporting a compact latent reasoning process. We evaluate the method across multiple model families, including LLaMA and Qwen, at different scales on mathematical reasoning benchmarks. Our approach consistently improves performance, especially on challenging multi-step tasks, approaching explicit CoT accuracy and outperforming prior iCoT distillation methods."
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<abstract>Implicit chain-of-thought (iCoT) methods aim to internalize reasoning in large language models, but often underperform explicit CoT prompting. We empirically find that hidden-state reasoning trajectories exhibit low-rank structure. Motivated by this observation, we propose a low-rank distillation framework that transfers reasoning by aligning teacher and student trajectories in a shared low-rank tensor subspace using first- and second-order statistics. The resulting formulation captures the global structure of reasoning while supporting a compact latent reasoning process. We evaluate the method across multiple model families, including LLaMA and Qwen, at different scales on mathematical reasoning benchmarks. Our approach consistently improves performance, especially on challenging multi-step tasks, approaching explicit CoT accuracy and outperforming prior iCoT distillation methods.</abstract>
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%0 Conference Proceedings
%T LoRi: Low-Rank Distillation for Implicit Reasoning
%A Solgi, Ryan
%A Tian, Jiayi
%A Zhang, Zheng
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F solgi-etal-2026-lori
%X Implicit chain-of-thought (iCoT) methods aim to internalize reasoning in large language models, but often underperform explicit CoT prompting. We empirically find that hidden-state reasoning trajectories exhibit low-rank structure. Motivated by this observation, we propose a low-rank distillation framework that transfers reasoning by aligning teacher and student trajectories in a shared low-rank tensor subspace using first- and second-order statistics. The resulting formulation captures the global structure of reasoning while supporting a compact latent reasoning process. We evaluate the method across multiple model families, including LLaMA and Qwen, at different scales on mathematical reasoning benchmarks. Our approach consistently improves performance, especially on challenging multi-step tasks, approaching explicit CoT accuracy and outperforming prior iCoT distillation methods.
%R 10.18653/2026.findings-emnlp.2322
%U https://aclanthology.org/2026.findings-emnlp.2322/
%U https://doi.org/10.18653/2026.findings-emnlp.2322
%P 49860-49871
Markdown (Informal)
[LoRi: Low-Rank Distillation for Implicit Reasoning](https://aclanthology.org/2026.findings-emnlp.2322/) (Solgi et al., Findings 2026)
ACL
- Ryan Solgi, Jiayi Tian, and Zheng Zhang. 2026. LoRi: Low-Rank Distillation for Implicit Reasoning. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 49860–49871, Budapest, Hungary. Association for Computational Linguistics.