Is RLHF suitable for all large models?

No, RLHF is not universally suitable for all large models. Its application depends heavily on specific goals and resource availability.

RLHF excels when aligning model outputs with complex human preferences and ethical guidelines. However, it requires extensive, high-quality human preference data for training the reward model. The process is computationally expensive and significantly more complex than simpler fine-tuning methods like SFT. Crucially, RLHF is most beneficial when explicit human value alignment, such as safety, helpfulness, and harmlessness, is the primary training objective, rather than just task performance.

Therefore, RLHF is highly recommended for large models deployed in sensitive domains like chatbots or content generation where nuanced human interaction and safety are paramount. Its implementation involves collecting human feedback, training a reward model, and iteratively fine-tuning the policy model using reinforcement learning. While powerful for alignment, less demanding tasks often achieve sufficient performance with standard supervised fine-tuning, making RLHF unnecessary for all models.