RLHF

RLHF, short for Reinforcement Learning from Human Feedback, is the alignment technique introduced by OpenAI in InstructGPT (2022) and used to train ChatGPT, Claude, Gemini, and most major commercial LLMs. The technique trains a reward model on pairwise human preferences ("response A is better than response B"), then uses reinforcement learning (typically PPO — Proximal Policy Optimization) to optimize the base model against the reward model. RLHF dramatically improves model helpfulness, harmlessness, and instruction following compared to SFT alone, but is expensive and operationally complex — requiring careful tuning of multiple models simultaneously and large preference datasets. Anthropic's Constitutional AI replaces some human feedback with AI-generated critiques following written principles. Newer techniques including DPO, KTO, and ORPO achieve similar alignment quality with simpler training pipelines, gradually replacing RLHF in many open-source workflows. AI governance teams document the reward model, preference dataset, and PPO hyperparameters as part of their alignment audit trail.

RLHF-aligned models in Centralpoint: Centralpoint routes generation to RLHF-aligned models from OpenAI, Anthropic, Google, and self-hosted alternatives — all in one model-agnostic platform. Tokens are metered per skill and audience, prompts stay local, and aligned-model chatbots deploy through one line of JavaScript with audit-ready governance.


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