RLHF

RLHF (Reinforcement Learning from Human Feedback) uses human preferences to align large language models with desired behaviors. The process trains a reward model on human comparisons ("which of these two responses is better?"), then uses reinforcement learning to optimize the LLM against that reward model. RLHF was central to making ChatGPT genuinely useful and safe — the underlying GPT-3.5 base model existed beforehand but felt much less polished. The technique is now widely used across Claude, Gemini, Llama Chat, and others. Alternatives like DPO (Direct Preference Optimization), KTO, and Constitutional AI have emerged as simpler approaches achieving similar results. RLHF is one of the highest-leverage steps in modern LLM development. Because human feedback can encode bias — based on who provided it and what kinds of responses they preferred — AI governance, AI ethics, and AI risk management programs scrutinize RLHF processes carefully. Disclosure of preference-data sources is becoming a standard responsible AI deployment expectation.

RLHF Aligns the Model; Centralpoint Aligns the Organisation: Oxcyon's Centralpoint AI Governance Platform layers enterprise oversight on top of RLHF-aligned models. Centralpoint is model-agnostic across ChatGPT, Gemini, Llama, and embedded options, meters every LLM transaction, keeps prompts and skills on-prem, and deploys chatbots to any portal via one JavaScript line.


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