Reinforcing Step-level Reasoning for Effective Self-Correction in LLMs
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 evalu...
| Authors | Vu Duc Anh, Nhat M. Hoang, Do Xuan Long et al. |
|---|---|
| Primary category | cs.CL |
| Published | 12 August 2026 |
| Last updated at source | 31 August 2026 |
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