[D] Is DPO still the best way to affordably fine-tune a model?

The paper "Your Language Model is Secretly a Reward Model: Direct Preference Optimization (DPO)" demonstrated that DPO can fine-tune LMs to align with human preferences as well as or better than existing methods" like RLHF.

Since this paper came out in May of 2023, I'm wondering if DPO is still considered to best approach to quickly and affordably finetune LLMs (particularly for startups).

direct preference optimization human language language model lms machinelearning optimization paper reward model rlhf