@inproceedings{li-etal-2025-small-models,
title = "Small Models Struggle to Learn from Strong Reasoners",
author = "Li, Yuetai and
Yue, Xiang and
Xu, Zhangchen and
Jiang, Fengqing and
Niu, Luyao and
Lin, Bill Yuchen and
Ramasubramanian, Bhaskar and
Poovendran, Radha",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.1301/",
doi = "10.18653/v1/2025.findings-acl.1301",
pages = "25366--25394",
ISBN = "979-8-89176-256-5",
abstract = "Large language models (LLMs) excel in complex reasoning tasks, and distilling their reasoning capabilities into smaller models has shown promise. However, we uncover an interesting phenomenon, which we term the Small Model Learnability Gap: small models (3B parameters) do not consistently benefit from long chain-of-thought (CoT) reasoning or distillation from larger models. Instead, they perform better when fine-tuned on shorter, simpler reasoning chains that better align with their intrinsic learning capacity. To address this, we propose Mix Distillation, a simple yet effective strategy that balances reasoning complexity by combining long and short CoT examples or reasoning from both larger and smaller models. Our experiments demonstrate that Mix Distillation significantly improves small model reasoning performance compared to training on either data alone. These findings highlight the limitations of direct strong model distillation and underscore the importance of adapting reasoning complexity for effective reasoning capability transfer."
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<abstract>Large language models (LLMs) excel in complex reasoning tasks, and distilling their reasoning capabilities into smaller models has shown promise. However, we uncover an interesting phenomenon, which we term the Small Model Learnability Gap: small models (3B parameters) do not consistently benefit from long chain-of-thought (CoT) reasoning or distillation from larger models. Instead, they perform better when fine-tuned on shorter, simpler reasoning chains that better align with their intrinsic learning capacity. To address this, we propose Mix Distillation, a simple yet effective strategy that balances reasoning complexity by combining long and short CoT examples or reasoning from both larger and smaller models. Our experiments demonstrate that Mix Distillation significantly improves small model reasoning performance compared to training on either data alone. These findings highlight the limitations of direct strong model distillation and underscore the importance of adapting reasoning complexity for effective reasoning capability transfer.</abstract>
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%0 Conference Proceedings
%T Small Models Struggle to Learn from Strong Reasoners
%A Li, Yuetai
%A Yue, Xiang
%A Xu, Zhangchen
%A Jiang, Fengqing
%A Niu, Luyao
%A Lin, Bill Yuchen
%A Ramasubramanian, Bhaskar
%A Poovendran, Radha
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F li-etal-2025-small-models
%X Large language models (LLMs) excel in complex reasoning tasks, and distilling their reasoning capabilities into smaller models has shown promise. However, we uncover an interesting phenomenon, which we term the Small Model Learnability Gap: small models (3B parameters) do not consistently benefit from long chain-of-thought (CoT) reasoning or distillation from larger models. Instead, they perform better when fine-tuned on shorter, simpler reasoning chains that better align with their intrinsic learning capacity. To address this, we propose Mix Distillation, a simple yet effective strategy that balances reasoning complexity by combining long and short CoT examples or reasoning from both larger and smaller models. Our experiments demonstrate that Mix Distillation significantly improves small model reasoning performance compared to training on either data alone. These findings highlight the limitations of direct strong model distillation and underscore the importance of adapting reasoning complexity for effective reasoning capability transfer.
%R 10.18653/v1/2025.findings-acl.1301
%U https://aclanthology.org/2025.findings-acl.1301/
%U https://doi.org/10.18653/v1/2025.findings-acl.1301
%P 25366-25394
Markdown (Informal)
[Small Models Struggle to Learn from Strong Reasoners](https://aclanthology.org/2025.findings-acl.1301/) (Li et al., Findings 2025)
ACL
- Yuetai Li, Xiang Yue, Zhangchen Xu, Fengqing Jiang, Luyao Niu, Bill Yuchen Lin, Bhaskar Ramasubramanian, and Radha Poovendran. 2025. Small Models Struggle to Learn from Strong Reasoners. In Findings of the Association for Computational Linguistics: ACL 2025, pages 25366–25394, Vienna, Austria. Association for Computational Linguistics.