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    <abstract>The OpenAI o1-series models have demonstrated that leveraging long-form Chain of Thought (CoT) can substantially enhance performance. However, the recursive thinking capabilities of Large Language Models (LLMs) remain limited, particularly in the absence of expert-curated data for distillation. In this paper, we propose AvR: Alignment via Refinement, a novel method aimed at unlocking the potential of LLMs for recursive reasoning through long-form CoT. AvR introduces a refinement process that integrates criticism and improvement actions, guided by differentiable learning techniques to optimize refinement-aware rewards. As a result, the synthesized multi-round data can be organized as a long refinement thought, further enabling test-time scaling. Experimental results show that AvR significantly outperforms conventional preference optimization methods. Notably, with only 3k synthetic samples, our method boosts the performance of the LLaMA-3-8B-Instruct model by over 20% in win rate on AlpacaEval 2.0. Our code is available at Github .</abstract>
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%0 Conference Proceedings
%T Unlocking Recursive Thinking of LLMs: Alignment via Refinement
%A Zhang, Haoke
%A Liang, Xiaobo
%A Wang, Cunxiang
%A Li, Juntao
%A Zhang, Min
%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 zhang-etal-2025-unlocking
%X The OpenAI o1-series models have demonstrated that leveraging long-form Chain of Thought (CoT) can substantially enhance performance. However, the recursive thinking capabilities of Large Language Models (LLMs) remain limited, particularly in the absence of expert-curated data for distillation. In this paper, we propose AvR: Alignment via Refinement, a novel method aimed at unlocking the potential of LLMs for recursive reasoning through long-form CoT. AvR introduces a refinement process that integrates criticism and improvement actions, guided by differentiable learning techniques to optimize refinement-aware rewards. As a result, the synthesized multi-round data can be organized as a long refinement thought, further enabling test-time scaling. Experimental results show that AvR significantly outperforms conventional preference optimization methods. Notably, with only 3k synthetic samples, our method boosts the performance of the LLaMA-3-8B-Instruct model by over 20% in win rate on AlpacaEval 2.0. Our code is available at Github .
%R 10.18653/v1/2025.findings-acl.582
%U https://aclanthology.org/2025.findings-acl.582/
%U https://doi.org/10.18653/v1/2025.findings-acl.582
%P 11169-11182

Markdown (Informal)

[Unlocking Recursive Thinking of LLMs: Alignment via Refinement](https://aclanthology.org/2025.findings-acl.582/) (Zhang et al., Findings 2025)

• Unlocking Recursive Thinking of LLMs: Alignment via Refinement (Zhang et al., Findings 2025)

ACL

• Haoke Zhang, Xiaobo Liang, Cunxiang Wang, Juntao Li, and Min Zhang. 2025. Unlocking Recursive Thinking of LLMs: Alignment via Refinement. In Findings of the Association for Computational Linguistics: ACL 2025, pages 11169–11182, Vienna, Austria. Association for Computational Linguistics.