Understand the mechanism

Collect comparisons or ratings of candidate answers, fit a preference/reward model, then optimize the policy (PPO, DPO, and relatives) so preferred answers become more likely while staying near the base model.

Read the alignment / RLHF path as a pipeline for the general-purpose chat model. At each stage, name the representation, the owner, and how information can be lost. Identifiers must mark prompt versions, model versions, indexes, and policies so “randomness” is not the default explanation for every bug in alignment / RLHF.

If raters prefer answer A over B in 62 of 100 pairs, the preference model should rank A higher on held-out pairs; a 50/50 split means the signal is noise for that slice. Scoped specifically to alignment / RLHF / general-purpose chat model / mechanism.

Keep the unit and the denominator visible when you discuss alignment / RLHF. A percentage without a base, or a latency without a percentile, hides the failure mode this chapter cares about on the general-purpose chat model.

Learned stages estimate; deterministic stages enforce. A fluent result from the general-purpose chat model does not prove alignment / RLHF used the right evidence. Preserve intermediates when privacy allows—candidate lists, traces, scores, citations—so you can see the first broken stage in the alignment / RLHF path.

Operational correctness for alignment / RLHF includes deadlines on the general-purpose chat model. If stages that feed this workload sum past the user’s patience, trim earlier—usually pack less, retrieve less, or parallelize—before blaming the model vendor for alignment / RLHF. Mechanism diagrams that ignore time are incomplete.

Also pin one numeric memory from this alignment / RLHF chapter: If raters prefer answer A over B in 62 of 100 pairs, the preference model should rank A higher on held-out pairs; a 50/50 split means the signal is noise for that slice. That number is not decoration; it is a template for how claims about alignment / RLHF on the general-purpose chat model should look in design docs. Scoped specifically to alignment / RLHF / general-purpose chat model / mechanism.

People confuse alignment / RLHF with neighboring buzzwords when debugging the general-purpose chat model. Before changing prompts, ask whether the broken stage was evidence gathering, the alignment / RLHF judgment itself, validation, or the product action. Fixing the wrong stage creates folklore (“we tried alignment / RLHF and it failed”) that blocks the next team on the general-purpose chat model. Scoped specifically to alignment / RLHF / general-purpose chat model / mechanism.