Understanding Reinforcement Learning with Human Feedback Part 4: Teaching Models Human Preferences
This article discusses the process of training models using human preferences in reinforcement learning. It explains how to modify a pre-existing model to create a reward model that assigns scores to responses based on human feedback. The training involves adjusting the model to give higher scores to preferred responses and lower scores to less preferred ones.
- ▪The article is part four of a series on reinforcement learning with human feedback.
- ▪A copied model is modified to create a reward model that assigns scores to responses.
- ▪The reward model is trained using human preference data to learn which responses are preferred.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 1207862) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Rijul Rajesh Posted on May 23 Understanding Reinforcement Learning with Human Feedback Part 4: Teaching Models Human Preferences #ai #machinelearning 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. To 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.
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