instruction tuning

Why a raw base model answers your question with three more questions, what RLHF actually buys, and the niche cases where customers should pick base. A tidy answer most candidates fumble.

Win-rate up, facts down is the signature of reward hacking: the policy is over-optimizing the preference proxy and drifting off the reference. The diagnosis (Goodhart on the reward), the immediate lever (tighten the KL/beta anchor), and the held-out factual gate that stops shipping the regression.

The naive pipeline gives you 10 million 'summarize this paragraph' pairs and a model that can only summarize paragraphs. The hard part is diversity and quality control at 100-billion-document scale, not the generation call.