https://nalar.dev/tags/llm-alignment/ Recent content in LLM Alignment on Nalar Hugo en-us Sun, 06 Sep 2026 00:00:00 +0700 https://nalar.dev/understand-direct-preference-optimization-for-llm-alignment/ Sun, 06 Sep 2026 00:00:00 +0700 https://nalar.dev/understand-direct-preference-optimization-for-llm-alignment/ <p>Supervised fine-tuning can teach a language model to imitate good answers, but many alignment problems are easier to express as comparisons: given two responses to the same prompt, which one is better?</p> <p>A preference dataset captures that signal as triples containing a prompt, a preferred response, and a rejected response. The challenge is turning those comparisons into model updates without treating a subjective preference as an ordinary next-token target.</p> <p><strong>Direct Preference Optimization (DPO)</strong> provides one practical answer. It trains a policy model to increase its relative preference for chosen responses over rejected responses while measuring that change against a fixed reference model. Unlike a common reinforcement-learning-from-human-feedback pipeline, standard DPO does not require training a separate reward model and then running a reinforcement-learning optimizer.</p>