Direct Preference Optimization for Chatbot Fine-Tuning: An Empirical Study
Quick Answer
This study introduces Direct Preference Optimization (DPO) for fine-tuning large language models, demonstrating enhanced computational efficiency and competitive performance.
Quick Take
Evaluations using BLEU, ROUGE, and cosine similarity metrics show effective learning, though training instability requires further investigation.
Key Points
- simplifies the training pipeline for .
- The approach improves computational efficiency during fine-tuning.
- Competitive performance was achieved in evaluations using standard metrics.
- Further investigation is needed to address training instability issues.
- Metrics used include BLEU, ROUGE, and cosine similarity.
Paper Resources
📖 Reader Mode
~1 min readAbstract: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.
| Comments: | 7 pages, 3 figures, 1 table. All authors contributed equally |
| Subjects: | Computation and Language (cs.CL); Machine Learning (cs.LG) |
| Cite as: | arXiv:2606.12881 [cs.CL] |
| (or arXiv:2606.12881v2 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.12881 arXiv-issued DOI via DataCite |
Submission history
From: Dezhi Yu [view email]
[v1]
Thu, 11 Jun 2026 04:15:54 UTC (170 KB)
[v2]
Fri, 12 Jun 2026 06:20:00 UTC (170 KB)
— Originally published at arxiv.org
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