{"slug": "understanding-reinforcement-learning-with-human-feedback-part-4-teaching-models", "title": "Understanding Reinforcement Learning with Human Feedback Part 4: Teaching Models Human Preferences", "summary": "The article explains how to train a reward model using human preference data in Reinforcement Learning with Human Feedback (RLHF). To create this model, a copy of the supervised fine-tuned model is modified by removing its unembedding layer and replacing it with a single output value, allowing it to assign reward scores to responses. The reward model is then trained to give higher scores to preferred responses and lower scores to less preferred ones, enabling it to learn human preferences.", "body_md": "In the previous article, we explored the part where we collect human preferences. In this article, we will see how to use this data to train the models.\nTo train a model that gives higher scores to preferred responses, we first make a copy of the model that has already gone through supervised fine-tuning.\nNext, we modify this copied model.\nWe remove the unembedding layer, which normally predicts the next token, and replace it with a single output value.\nThe result is a new model called a reward model.\nInstead of generating text, this model learns to assign a reward score to a response.\nWe can now train this reward model using the human preference data we collected earlier.\nFor a preferred response, we train the model to produce a higher reward value.\nFor a less preferred response, we train the model to produce a lower reward value or a negative reward.\nFor example:\nOver time, the reward model learns what kinds of responses humans tend to prefer.\nWe will continue further in the next article\nLooking for an easier way to install tools, libraries, or entire repositories?\nTry Installerpedia: a community-driven, structured installation platform that lets you install almost anything with minimal hassle and clear, reliable guidance.\nJust run:\nipm install repo-name\n… and you’re done! 🚀", "url": "https://wpnews.pro/news/understanding-reinforcement-learning-with-human-feedback-part-4-teaching-models", "canonical_source": "https://dev.to/rijultp/understanding-reinforcement-learning-with-human-feedback-part-4-teaching-models-human-preferences-m7f", "published_at": "2026-05-23 19:25:30+00:00", "updated_at": "2026-05-23 19:32:02.173040+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "large-language-models", "research"], "entities": ["Reinforcement Learning with Human Feedback", "Installerpedia"], "alternates": {"html": "https://wpnews.pro/news/understanding-reinforcement-learning-with-human-feedback-part-4-teaching-models", "markdown": "https://wpnews.pro/news/understanding-reinforcement-learning-with-human-feedback-part-4-teaching-models.md", "text": "https://wpnews.pro/news/understanding-reinforcement-learning-with-human-feedback-part-4-teaching-models.txt", "jsonld": "https://wpnews.pro/news/understanding-reinforcement-learning-with-human-feedback-part-4-teaching-models.jsonld"}}