DPO
DPO (Direct Preference Optimization) is an alignment technique for large language models that directly optimizes a language model policy from human preference data, without training a separate reward model or…
Training & Optimization
Explore Training & Optimization through related topics and the articles other pages reference most.
Explore articles
Browse subtopics: AI Alignment
Articles that also belong to these categories. Counts cover all of Training & Optimization.
All articles (145)Machine Learning (70)Deep Learning (41)Large Language Models (18)AI Infrastructure (9)Mathematics (9)AI Inference (8)Reinforcement Learning (8)AI Alignment (7)Developer Tools (7)Natural Language Processing (7)Open Source AI (7)MLOps (6)Statistics (6)NVIDIA (4)AI Hardware (3)Artificial Intelligence (3)Computer Vision (3)Reasoning Models (3)Algorithms (2)Chinese AI (2)Data & Datasets (2)Generative AI (2)Meta AI (2)Model Evaluation (2)Robotics (2)Transformer Models (2)AI Agents (1)AI Code Generation (1)AI Models (1)AI Research (1)AI Tools & Products (1)Enterprise AI (1)Google (1)Google DeepMind (1)Humanoid Robots (1)Microsoft (1)Mixture of Experts (1)Model Architecture (1)Neural Networks (1)OpenAI (1)Small Language Models (1)World Models (1)
Showing 1-7 of 7 articles
InstructGPT
InstructGPT is a family of language models released by OpenAI in January 2022 that take the base GPT-3 and fine-tune it to follow user instructions more helpfully, truthfully, and with less toxic output, using…
KTO
KTO (Kahneman-Tversky Optimization) is a method for aligning large language models with human feedback using only a binary signal of whether a model output is desirable or undesirable, rather than the paired…
RLOO (REINFORCE Leave-One-Out)
RLOO (REINFORCE Leave-One-Out) is an online reinforcement learning algorithm for aligning large language models with reward signals such as those derived from human preferences.
SPIN (Self-Play Fine-Tuning)
SPIN (Self-Play fIne-tuNing) is a post-training method for large language models introduced by researchers at the University of California, Los Angeles (UCLA) in January 2024.
Self-Rewarding Language Models
Self-Rewarding Language Models (SRLM) is an iterative alignment method in which a single large language model alternately plays the role of policy (generating candidate responses to user prompts) and reward…
SimPO
SimPO (Simple Preference Optimization) is a reference-free offline preference learning algorithm for aligning large language models with human preferences.