Recent advancements in Large Vision-Language Models (LVLMs) have greatly improved their ability to understand both visual and text information. However, a common problem in LVLMs is confirmation bias, where models tend to repeat previous assumptions and follow earlier viewpoints instead of reflecting and correcting themselves. This problem is more common in smaller-scale LVLMs, as they are usually fine-tuned with training data that is mostly positive, focusing on generating coherent dialogue. To address this issue, we introduce ReCoT, a method designed to mitigate confirmation bias in smaller-scale LVLMs through Reflective Self-Correction Training.The method follows a two-stage SFT-DPO paradigm: the first SFT stage aims to cultivate the model's reflective correction abilities, while the DPO stage focuses on enhancing the consistency between answers and reflections. Specifically, we construct dialogue-based reflective samples, which serve as adversarial samples during SFT. In this process, the model is initially presented with a potentially incorrect answer, followed by a reflection and correction phase to generate the final answer. To enhance answer-reflection consistency, we propose the consistency direct preference optimization. To comprehensively evaluate the effectiveness of our ReCoT, we introduce a set of novel metrics to measure the accuracy of the reflection and correction process. Extensive experiments show that ReCoT enables LVLM to engage in robust self-reflection and error correction and reduce confirmation bias. Code will be released.