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Another alternative to RLHF called Direct Preference Optimization (DPO) has been proposed to learn human preferences. Like RLHF, it has been applied to align pre-trained large language models using human-generated preference data. Unlike RLHF, however, which first trains a separate intermediate model to understand what good outcomes look like ...
Direct preference optimization, a technique for aligning AI models with human preferences; Double pushout graph rewriting, in computer science; Other.
Preference learning is a subfield of machine learning that focuses on modeling and predicting preferences based on observed preference information. [1] Preference learning typically involves supervised learning using datasets of pairwise preference comparisons, rankings, or other preference information.
Multi-objective optimization or Pareto optimization (also known as multi-objective programming, vector optimization, multicriteria optimization, or multiattribute optimization) is an area of multiple-criteria decision making that is concerned with mathematical optimization problems involving more than one objective function to be optimized simultaneously.
The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is a multi-criteria decision analysis method, which was originally developed by Ching-Lai Hwang and Yoon in 1981 [1] with further developments by Yoon in 1987, [2] and Hwang, Lai and Liu in 1993. [3]
Proximal policy optimization (PPO) is a reinforcement learning (RL) algorithm for training an intelligent agent. Specifically, it is a policy gradient method , often used for deep RL when the policy network is very large.
The leximin-order is also used for Multi-objective optimization, [6] for example, in optimal resource allocation, [7] location problems, [8] and matrix games. [ 9 ] It is also studied in the context of fuzzy constraint solving problems .
That is, they proved that an agent is (VNM-)rational if and only if there exists a real-valued function u defined by possible outcomes such that every preference of the agent is characterized by maximizing the expected value of u, which can then be defined as the agent's VNM-utility (it is unique up to affine transformations i.e. adding a ...