In this paper, we propose Direct Preference Optimization (DPO), which directly utilizes preferences to optimize policies without the complications of a traditional reinforcement learning setup. This approach simplifies policy optimization by eliminating the need for a reward learning phase, which is often a bottleneck in applying RLHF to large-scale language model fine-tuning.
Our findings demonstrate that the Direct Preference Optimization method not only outperforms existing RLHF techniques in terms of convergence speed and ease of implementation, but it also allows for the extraction of optimal policies in closed form. This is particularly advantageous in settings like language model fine-tuning where rapid iterations are crucial.
#direct-preference-optimization #reinforcement-learning #language-models #policy-optimization #machine-learning
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