Direct Preference Optimization: Your Language Model is Secretly a Reward Model | HackerNoon

The paper discusses the challenges in controlling large-scale unsupervised language models, noting that techniques relying on reinforcement learning from human feedback (RLHF) can often be complex and unstable.

Existing methods for gaining steerability in language models typically utilize human labels to fine-tune these models, highlighting the complexities associated with reinforcement learning and preference alignment.

Read at Hackernoon