Auditing Preference Biases and Fine-Tuning Language Models with Direct Preference Optimization on Anthropic HH-RLHF Using TRL and LoRA

Summary

<p>This tutorial provides an end-to-end workflow for fine-tuning language models using Direct Preference Optimization (DPO). We demonstrate how to audit the Anthropic HH-RLHF dataset for structural and length-based biases, implement a robust training pipeline using TRL and LoRA, and evaluate model performance to ensure genuine preference learning rather than reliance on lexical shortcuts.</p> <p>The post <a href="https://www.marktechpost.com/2026/08/20/auditing-preference-biases-and-fine-tuning-language-models-with-direct-preference-optimization-on-anthropic-hh-rlhf-using-trl-and-lora/">Auditing Preference Biases and Fine-Tuning Language Models with Direct Preference Optimization on Anthropic HH-RLHF Using TRL and LoRA</a> appeared first on <a href="https://www.marktechpost.com">MarkTechPost</a>.</p>