RLHF Explained: The Human-Feedback Era Is Already Ending
RLHF (Reinforcement Learning from Human Feedback) is the training step that turns a raw, pretrained language model into a usable assistant, using human rankings of outputs to train a reward model, then optimizing the base model against that reward model, typically via PPO. It's the technique behind ChatGPT, Claude, and Gemini's instruction-following behavior, but as of mid-2026 the industry has largely moved on to cheaper successors: DPO for general preference tuning, RLAIF for AI-generated feedback, and RLVR for verifiable-reward reasoning models. The bigger signal is that alignment is getting radically cheaper and more DIY, which changes who can afford to do it well. For PMs, that means the competitive moat of "better-behaved AI" is eroding faster than most roadmaps assume, and in-house preference tuning is now within reach of teams that could never have run an RL pipeline.
