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Author: mrbullwinkle

articles/ai-services/openai/whats-new.md

@@ -23,7 +23,7 @@ This article provides a summary of the latest releases and major documentation u
 
 ### Preference fine-tuning (preview)
 
-[Direct preference optimization (DPO)](./how-to/fine-tuning.md#direct-preference-optimization-dpo) is a new alignment technique for large language models, designed to adjust model weights based on human preferences. Unlike reinforcement learning from human feedback (RLHF), DPO does not require fitting a reward model and uses simpler data (binary preferences) for training. This method is computationally lighter and faster, making it equally effective at alignment while being more efficient. DPO is especially useful in scenarios where subjective elements like tone, style, or specific content preferences are important. We’re excited to announce the public preview of DPO in Azure OpenAI Service, starting with the `gpt-4o-2024-08-06` model.
+[Direct preference optimization (DPO)](./how-to/fine-tuning.md#direct-preference-optimization-dpo-preview) is a new alignment technique for large language models, designed to adjust model weights based on human preferences. Unlike reinforcement learning from human feedback (RLHF), DPO does not require fitting a reward model and uses simpler data (binary preferences) for training. This method is computationally lighter and faster, making it equally effective at alignment while being more efficient. DPO is especially useful in scenarios where subjective elements like tone, style, or specific content preferences are important. We’re excited to announce the public preview of DPO in Azure OpenAI Service, starting with the `gpt-4o-2024-08-06` model.
 
 For fine-tuning model region availability, see the [models page](./concepts/models.md#fine-tuning-models).