alignment-approaches-are-mature-enough-for-reliable-deployment

Antecedents (all must be IN):

• IN alignment-diversified-into-three-independent-paradigms — LLM alignment diversified from a single RLHF pipeline into three independent paradigms — full mathematical RLHF, direct preference optimization (DPO/IPO/KTO), and Constitutional AI — each eliminating different sources of complexity while preserving alignment quality.
• IN rlhf-has-complete-mathematical-pipeline — RLHF has a fully specified mathematical pipeline: Bradley-Terry loss trains the reward model from comparisons, KL divergence constrains policy drift, and PPO's clipped surrogate optimizes the policy — each component addressing a distinct failure mode.

Unless (any of these IN defeats this justification):

• IN rlhf-implementation-details-critical — Naive PPO implementations fail in RLHF; dozens of engineering details including reward normalization, KL penalty tuning, advantage estimation (GAE), learning rate schedules, and batch size significantly affect training stability and performance.