{"slug": "understanding-reinforcement-learning-with-human-feedback-part-3-collecting-human", "title": "Understanding Reinforcement Learning with Human Feedback Part 3: Collecting Human Preferences", "summary": "In Reinforcement Learning with Human Feedback (RLHF), human preference collection involves generating multiple responses for a given prompt and asking people to select the preferred one, which is faster than manual response writing. This collected preference data is then used to train the model to assign higher scores to preferred responses, gradually aligning its outputs with human preferences.", "body_md": "In the [previous article](https://dev.to/rijultp/understanding-reinforcement-learning-with-human-feedback-part-2-aligning-pretrained-models-58ho) we explored the concept of aligning the pretrained model. Now, we will look at the next component: human preference collection.\n\nThe first step in understanding **RLHF** is to understand that, given a specific prompt, a model can generate different responses.\n\nOne way to generate a response is to configure the model to always select the token with the **highest output value** at every step.\n\nIn this case, the model will generate the same response every single time for a given prompt.\n\n## Generating Different Responses\n\nInstead of always selecting the highest-value token, we can also use the outputs of the **softmax function** as probabilities for selecting tokens.\n\nIn this approach:\n\n- the token with the highest probability is\n**more likely** to be selected - but other tokens still have a chance of being selected\n\nAs a result, the model can generate **different responses** for the same prompt.\n\n## Collecting Human Preferences\n\nSince a model can generate multiple responses, we can create **pairs of responses** for the same prompt.\n\nWe can then ask people which response they prefer.\n\nFor example, given two possible answers, a person can simply choose the better one.\n\nCollecting preferences like this is much faster than asking people to manually write responses for every prompt.\n\nThis preference collection process is the **“Human Feedback”** part of **RLHF**.\n\n## Using Preference Data\n\nOnce we collect preference data, we can use it to train the model so that it assigns **higher scores to preferred responses** and lower scores to less preferred ones.\n\nOver time, this helps the model generate responses that better match human preferences.\n\nIn the next article, we will explore how to train the model using this preference data.\n\n**Looking for an easier way to install tools, libraries, or entire repositories?**\n\nTry **Installerpedia**: a **community-driven, structured installation platform** that lets you install almost anything with **minimal hassle** and **clear, reliable guidance**.\n\nJust run:\n\n```\nipm install repo-name\n```\n\n… and you’re done! 🚀", "url": "https://wpnews.pro/news/understanding-reinforcement-learning-with-human-feedback-part-3-collecting-human", "canonical_source": "https://dev.to/rijultp/understanding-reinforcement-learning-with-human-feedback-part-3-collecting-human-preferences-6cl", "published_at": "2026-05-20 19:05:25+00:00", "updated_at": "2026-05-20 19:33:40.347749+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "large-language-models", "research"], "entities": ["RLHF"], "alternates": {"html": "https://wpnews.pro/news/understanding-reinforcement-learning-with-human-feedback-part-3-collecting-human", "markdown": "https://wpnews.pro/news/understanding-reinforcement-learning-with-human-feedback-part-3-collecting-human.md", "text": "https://wpnews.pro/news/understanding-reinforcement-learning-with-human-feedback-part-3-collecting-human.txt", "jsonld": "https://wpnews.pro/news/understanding-reinforcement-learning-with-human-feedback-part-3-collecting-human.jsonld"}}