DPO: Direct Preference Optimization focuses on directly optimizing language models to adhere to human preferences. It operates without explicit reward modeling or reinforcement learning, simplifying the training process. DPO optimizes the same objectives as RLHF but with a straightforward binary cross-entropy loss.
Sep 4, 2024
The method introduces a formal predicate mode declaration for designating certain predicates as optimization predicates, and uses preference rules for stating ...
ABSTRACT. Traditional constraint programming specifies an optimiza- tion problem by using a set of constraints and minimizing.
May 29, 2023 · Our experiments show that DPO can fine-tune LMs to align with human preferences as well as or better than existing methods. Notably, fine-tuning ...
Traditional constraint programming specifies an optimization problem by using a set of constraints and minimizing (or maximizing) objective functions.
Feb 17, 2024 · A look at the “Direct Preference Optimization: Your Language Model is Secretly a Reward Model” paper and its findings.
Feb 16, 2024 · In this paper, we propose a generalization of DPO, termed DPO with an offset (ODPO), that does not treat every preference pair equally during fine-tuning.
Missing: mode- | Show results with:mode-
Nov 5, 2023 · Direct Preference Optimization is a stable, performant, and computationally lightweight algorithm. Unlike its predecessor, RLHF, DPO eliminates ...
Our method uses mode declarations to designate certain predicates as optimization predicates, and uses preference rules for stating the criteria for determining ...
Our experiments show that DPO can fine-tune LMs to align with human preferences as well as or better than existing methods.
Missing: directed | Show results with:directed
People also search for