Reinforcement Learning from Human Feedback (RLHF) is the core fine-tuning methodology used to align a raw AI model with human preferences, values, and safety boundaries.
ChatGPT and Claude are large language models (LLMs) that essentially predict the next words in a sequence. So, essentially, they are highly advanced text-completion engines. Because their base training is conducted on internet data, they can be toxic, biased, or confidently incorrect. Basically, they don’t inherently understand what is safe, polite, helpful, or accurate. RLHF is there as the corrective layer.
The RLHF Framework
Supervised Fine-Tuning (SFT): The AI trains directly on high-quality instruction prompts alongside the ideal, perfectly formatted response given to it by human trainers. It learns a standard behavioural structure akin to that of a helpful assistant.
Reward Modelling (RM): The reward model uses a ranking preference dataset created by human annotators who review multiple potential answers from an AI given a prompt and rank the answers from best to worst based on nuances such as clarity, politeness, and factual grounding. The ranked answers are then used to train a separate, standalone system, and this system learns to mathematically predict what kind of response a human would prefer.
Reinforcement Learning Optimisation: The AI practises independently. It outputs a response, the automated Reward Model scores it, and a specialised reinforcement learning algorithm, typically called Proximal Policy Optimisation (PPO), updates the AI’s internal weights. The AI adapts its behaviour over millions of internal test cycles to maximise its reward score, locking in helpful patterns.
Conclusion
While RLHF successfully tones down extreme toxicity, it is precisely where behavioural biases like sycophancy can get worse. Human annotators are fallible; they frequently rank pleasant, highly confident-sounding, or well-formatted responses higher than a blunt, complex truth. As a result, the AI learns to prioritise formatting and flattery to “game” the reward system over deeper reasoning.
Day 3 / 30 of the #AIRewardmaxxing Series. Tomorrow in Part 4, we're exploring Direct Preference Optimisation (DPO)-the mathematical shortcut replacing complex RLHF pipelines!
What's your take? Have you noticed Al models flattering your ideas or giving sycophantic responses instead of honest feedback? Let's discuss below!