Direct preference optimization: Your language model is secretly a reward model
By Rafael Rafailov, Archit Sharma, Eric Mitchell, Stefano Ermon, Christopher D. Manning, Chelsea Finn
"Proposes Direct Preference Optimization (DPO) as an alternative to PPO-based RLHF. Simplifies alignment by optimizing the policy directly from human preference data."
Abstract
We present Direct Preference Optimization (DPO), a stable, performant, and computationally lightweight algorithm for steering LLMs to align with human preferences. DPO avoids the instability of traditional RLHF by mathematically optimizing the policy directly from preference data without training an explicit reward model.
Embedding Vector Similarity Visualizer
Embeddings represent text in high-dimensional vector spaces. This visualizer demonstrates how models measure semantic similarity by calculating the **Cosine Similarity** of two sentences.
Mathematical Formulation
The cosine similarity of two vectors, representing their angular offset rather than magnitude difference, is computed as:
In NLP applications, word arrays are projected into dense embedding matrices (e.g. 1536 dimensions). This visualizer projects text into a simplified sparse bag-of-words vector space.
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