Direct Preference Optimization for Chatbot Fine-Tuning: An Empirical Study

Researchers have demonstrated that Direct Preference Optimization (DPO) simplifies the fine-tuning pipeline for large language models while improving computational efficiency and achieving competitive performance. The empirical study, evaluated using BLEU, ROUGE, and cosine similarity metrics, showed effective learning and convergence but also identified training instability requiring further investigation. The findings suggest DPO could streamline chatbot development, though the observed instability poses a challenge for reliable deployment.

Computer Science Computation and Language Submitted on 11 Jun 2026 Title:Direct Preference Optimization for Chatbot Fine-Tuning: An Empirical Study View PDF /pdf/2606.12881 HTML experimental https://arxiv.org/html/2606.12881v1 Abstract:We present an approach to fine-tuning large language models using Direct Preference Optimization DPO , a reinforcement learning technique. Our experimental results demonstrate that DPO simplifies the training pipeline, improves computational efficiency, and achieves competitive performance. The evaluation using BLEU, ROUGE, and cosine similarity metrics indicates effective learning and convergence, though further investigation is needed to address observed training instability. References & Citations Loading... Bibliographic and Citation Tools Bibliographic Explorer What is the Explorer? https://info.arxiv.org/labs/showcase.html arxiv-bibliographic-explorer Connected Papers What is Connected Papers? https://www.connectedpapers.com/about Litmaps What is Litmaps? https://www.litmaps.co/ scite Smart Citations What are Smart Citations? https://www.scite.ai/ Code, Data and Media Associated with this Article alphaXiv What is alphaXiv? https://alphaxiv.org/ CatalyzeX Code Finder for Papers What is CatalyzeX? https://www.catalyzex.com DagsHub What is DagsHub? https://dagshub.com/ Gotit.pub What is GotitPub? http://gotit.pub/faq Hugging Face What is Huggingface? https://huggingface.co/huggingface ScienceCast What is ScienceCast? https://sciencecast.org/welcome Demos Recommenders and Search Tools Influence Flower What are Influence Flowers? https://influencemap.cmlab.dev/ CORE Recommender What is CORE? https://core.ac.uk/services/recommender arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs https://info.arxiv.org/labs/index.html .