🤖 AI Summary
This work addresses the limitations of traditional reinforcement learning–based approaches for fine-tuning conversational agents, which often suffer from procedural complexity, high computational costs, and training instability. To overcome these challenges, the study proposes employing Direct Preference Optimization (DPO) as an alternative to reinforcement learning for efficiently fine-tuning large language models. The results demonstrate that DPO significantly simplifies the training pipeline and enhances training efficiency while maintaining strong generative performance, as evidenced by competitive scores on BLEU, ROUGE, and cosine similarity metrics. The research validates DPO’s effectiveness as a viable substitute for reinforcement learning in preference-based alignment and further identifies residual training instabilities under practical conditions, thereby providing an empirical foundation for future refinements.
📝 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.