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Yapay ZekâÖnerilenAIML-LM-013

Preference Optimization

Learning from comparisons: reward models, RLHF, and the DPO idea.

Bu konunun henüz Türkçe çevirisi yok, bu yüzden İngilizce gösteriliyor.

Zorluk
3 / 5
Önem
4 / 5
Çalışma süresi
1–2 sa
Çalışma türü
Karma

Neden önemli

The learner can narrate the reward-model-plus-policy loop and state what DPO skips.

Bu konuya giden rota

Rota AIML-LM-013
What Is a Model? Tasks: Regression, Classification, and Beyond Features and Targets Classification Framing Linear Regression I: Prediction and Residuals Embeddings Tokens and Tokenization Scores and Logits Loss and Fitting: Squared Error Multiclass Scores and Softmax The Artificial Neuron Autoregression Objective Functions and Empirical Risk Activation Functions Next-Token Prediction Gradient Descent in Machine Learning Likelihood The Multilayer Perceptron Cross-Entropy Loss Stochastic and Mini-batch Optimization The Language-Modeling Objective Transfer Learning Pretraining Fine-Tuning Supervised Fine-Tuning Preference Optimization