aiqbench analysis
This research introduces SFS-DPO, a reinforcement learning framework designed to enhance step-level reasoning and self-correction capabilities in large language models. By incorporating explanatory rationales for error verification, the proposed method demonstrates improved robustness across various domains compared to existing baselines. The work addresses a core challenge in advancing LLM reliability by formalizing how models can autonomously detect and rectify their own mistakes during inference.
Reinforcing Step-level Reasoning for Effective Self-Correction in LLMs
arXiv:2608.11573v2 Announce Type: replace-cross Abstract: Achieving effective self-correction, where models verify and correct their own mistakes, remains a fundamental challenge for large language models (LLMs). In this work, we propose Self-Fix Step-DPO (SFS-DPO), a reinforcement learning based, two-stage framework for step-level self-verification and self-correction. The first stage strengthens step-level reasoning via step-level preference optimization, while the second stage explicitly trains models to self-verify and self-correct. We further introduce a teacher-assisted variant, SFS-DPO-R, which incorporates explanatory rationales for error verification to provide stronger corrective signals. Comprehensive in-domain and out-of-domain evaluations across multiple LLMs demonstrate that SFS-DPO and SFS-DPO-R consistently outperform prior step-level training baselines. Our analysis further reveals improvements in self-correction frequency and effectiveness, highlighting the importance of strengthening step-level reasoning for robust performance.
Abstract:Achieving effective self-correction, where models verify and correct their own mistakes, remains a fundamental challenge for large language models (LLMs). In this work, we propose Self-Fix Step-DPO (SFS-DPO), a reinforcement learning based, two-stage framework for step-level self-verification and self-correction. The first stage strengthens step-level reasoning via step-level preference optimization, while the second stage explicitly trains models to self-verify and self-correct. We further introduce a teacher-assisted variant, SFS-DPO-R, which incorporates explanatory rationales for error verification to provide stronger corrective signals. Comprehensive in-domain and out-of-domain evaluations across multiple LLMs demonstrate that SFS-DPO and SFS-DPO-R consistently outperform prior step-level training baselines. Our analysis further reveals improvements in self-correction frequency and effectiveness, highlighting the importance of strengthening step-level reasoning for robust performance.
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
arXiv:2608.11573 [cs.CL]
(or arXiv:2608.11573v2 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2608.11573 Focus to learn more arXiv-issued DOI via DataCite
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