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…
AI Alignment
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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.