Guide Audits Preference Biases While Fine-Tuning Qwen2.5 With DPO

Anthropic’s HH-RLHF dataset pairs preferred and rejected responses and is widely used to align language models with human judgments. Direct Preference Optimization, or DPO, offers a simpler alternative to reinforcement learning from human feedback by training directly on those comparisons. The approach can still reproduce structural biases, however, if models learn shortcuts tied to response length, tone or recurring vocabulary instead of genuine helpfulness and safety.

The new guide applies DPO to Alibaba’s Qwen2.5 after auditing HH-RLHF for distribution imbalances and lexical signals that may distort preference labels. It uses Hugging Face’s TRL framework and Low-Rank Adaptation, or LoRA, to reduce the parameters updated during fine-tuning, then evaluates model performance after training. The source material does not specify a publication date, model size, training cost or numerical benchmark results, positioning the work primarily as a reproducible workflow for bias diagnosis and preference optimization.