@inproceedings{li-etal-2025-reflectevo,
title = "{R}eflect{E}vo: Improving Meta Introspection of Small {LLM}s by Learning Self-Reflection",
author = "Li, Jiaqi and
Dong, Xinyi and
Liu, Yang and
Yang, Zhizhuo and
Wang, Quansen and
Wang, Xiaobo and
Zhu, Song-Chun and
Jia, Zixia and
Zheng, Zilong",
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.871/",
doi = "10.18653/v1/2025.findings-acl.871",
pages = "16948--16966",
ISBN = "979-8-89176-256-5",
abstract = "We present a novel pipeline, ReflectEvo, to demonstrate that small language models (SLMs) can enhance meta introspection through reflection learning. This process iteratively generates self-reflection for self-training, fostering a continuous and self-evolving process. Leveraging this pipeline, we construct ReflectEvo-460k, a large-scale, comprehensive, self-generated reflection dataset with broadened instructions and diverse multi-domain tasks. Building upon this dataset, we demonstrate the effectiveness of reflection learning to improve SLMs' reasoning abilities using SFT and DPO with remarkable performance, substantially boosting Llama-3 from 52.4{\%} to 71.2{\%} and Mistral from 44.4{\%} to 71.1{\%}. It validates that ReflectEvo can rival or even surpass the reasoning capability of the three prominent open-sourced models on BIG-bench without distillation from superior models or fine-grained human annotation. We further conduct a deeper analysis of the high quality of self-generated reflections and their impact on error localization and correction. Our work highlights the potential of continuously enhancing the reasoning performance of SLMs through iterative reflection learning in the long run."
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<abstract>We present a novel pipeline, ReflectEvo, to demonstrate that small language models (SLMs) can enhance meta introspection through reflection learning. This process iteratively generates self-reflection for self-training, fostering a continuous and self-evolving process. Leveraging this pipeline, we construct ReflectEvo-460k, a large-scale, comprehensive, self-generated reflection dataset with broadened instructions and diverse multi-domain tasks. Building upon this dataset, we demonstrate the effectiveness of reflection learning to improve SLMs’ reasoning abilities using SFT and DPO with remarkable performance, substantially boosting Llama-3 from 52.4% to 71.2% and Mistral from 44.4% to 71.1%. It validates that ReflectEvo can rival or even surpass the reasoning capability of the three prominent open-sourced models on BIG-bench without distillation from superior models or fine-grained human annotation. We further conduct a deeper analysis of the high quality of self-generated reflections and their impact on error localization and correction. Our work highlights the potential of continuously enhancing the reasoning performance of SLMs through iterative reflection learning in the long run.</abstract>
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%0 Conference Proceedings
%T ReflectEvo: Improving Meta Introspection of Small LLMs by Learning Self-Reflection
%A Li, Jiaqi
%A Dong, Xinyi
%A Liu, Yang
%A Yang, Zhizhuo
%A Wang, Quansen
%A Wang, Xiaobo
%A Zhu, Song-Chun
%A Jia, Zixia
%A Zheng, Zilong
%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-reflectevo
%X We present a novel pipeline, ReflectEvo, to demonstrate that small language models (SLMs) can enhance meta introspection through reflection learning. This process iteratively generates self-reflection for self-training, fostering a continuous and self-evolving process. Leveraging this pipeline, we construct ReflectEvo-460k, a large-scale, comprehensive, self-generated reflection dataset with broadened instructions and diverse multi-domain tasks. Building upon this dataset, we demonstrate the effectiveness of reflection learning to improve SLMs’ reasoning abilities using SFT and DPO with remarkable performance, substantially boosting Llama-3 from 52.4% to 71.2% and Mistral from 44.4% to 71.1%. It validates that ReflectEvo can rival or even surpass the reasoning capability of the three prominent open-sourced models on BIG-bench without distillation from superior models or fine-grained human annotation. We further conduct a deeper analysis of the high quality of self-generated reflections and their impact on error localization and correction. Our work highlights the potential of continuously enhancing the reasoning performance of SLMs through iterative reflection learning in the long run.
%R 10.18653/v1/2025.findings-acl.871
%U https://aclanthology.org/2025.findings-acl.871/
%U https://doi.org/10.18653/v1/2025.findings-acl.871
%P 16948-16966
Markdown (Informal)
[ReflectEvo: Improving Meta Introspection of Small LLMs by Learning Self-Reflection](https://aclanthology.org/2025.findings-acl.871/) (Li et al., Findings 2025)
ACL
- Jiaqi Li, Xinyi Dong, Yang Liu, Zhizhuo Yang, Quansen Wang, Xiaobo Wang, Song-Chun Zhu, Zixia Jia, and Zilong Zheng. 2025. ReflectEvo: Improving Meta Introspection of Small LLMs by Learning Self-Reflection. In Findings of the Association for Computational Linguistics: ACL 2025, pages 16948–16966, Vienna, Austria. Association for Computational Linguistics.