{"slug": "understanding-reinforcement-learning-with-human-feedback-part-5-training-the", "title": "Understanding Reinforcement Learning with Human Feedback Part 5: Training the Reward Model with Loss Functions", "summary": "OpenAI trained a reward model using a loss function that does not require defining ideal reward values in advance. The loss function, introduced in OpenAI's 2022 paper, guides the model to assign higher rewards to preferred responses by maximizing the difference between rewards for better and worse outputs. Once trained, the reward model is used to further train the original model that underwent supervised fine-tuning.", "body_md": "In the [previous article](https://dev.to/rijultp/understanding-reinforcement-learning-with-human-feedback-part-4-teaching-models-human-preferences-m7f), we created a **reward model**. In this article, we will continue exploring how this model is trained.\n\nOne important thing to note is that we do **not** need to define the ideal reward values in advance.\n\nInstead, the model learns to determine appropriate rewards on its own.\n\nTo train the reward model, OpenAI used the following loss function in their 2022 paper:\n\nThis loss function helps the model learn good reward values **without us explicitly defining what the rewards should be**.\n\nWhere:\n\nIdeally, we want:\n\nReward_better - Reward_worse\n\nto be a **large positive number**.\n\nThe difference between the rewards is first passed through a **sigmoid function**.\n\nFor any input value, the sigmoid function outputs a value between **0 and 1**.\n\nIn an ideal case, we want the sigmoid output to be **close to 1**. This happens when the preferred response receives a much higher reward than the worse response.\n\nThe output of the sigmoid function is then passed through a **log function**.\n\nIn the ideal case, this produces a relatively high value.\n\nFinally, we multiply the result by **-1**.\n\nThis turns the equation into a loss function that optimization algorithms can minimize during training.\n\nThe interesting part is that we never explicitly tell the model:\n\nInstead, the loss function naturally guides the model toward assigning rewards in a way that makes preferred responses score higher than worse ones.\n\nOnce the reward model is fully trained, we can use it to train the original model that only went through **supervised fine-tuning**.\n\nIn the next article, we will explore how the reward model is used to further train the original model.\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-5-training-the", "canonical_source": "https://dev.to/rijultp/understanding-reinforcement-learning-with-human-feedback-part-5-training-the-reward-model-with-3g37", "published_at": "2026-05-25 19:15:00+00:00", "updated_at": "2026-05-25 19:33:31.006562+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "large-language-models", "neural-networks", "ai-research"], "entities": ["OpenAI"], "alternates": {"html": "https://wpnews.pro/news/understanding-reinforcement-learning-with-human-feedback-part-5-training-the", "markdown": "https://wpnews.pro/news/understanding-reinforcement-learning-with-human-feedback-part-5-training-the.md", "text": "https://wpnews.pro/news/understanding-reinforcement-learning-with-human-feedback-part-5-training-the.txt", "jsonld": "https://wpnews.pro/news/understanding-reinforcement-learning-with-human-feedback-part-5-training-the.jsonld"}}