Sequential Preference-Based Optimization
Many real-world engineering problems rely on human preferences to guide their design and optimization. We present PrefOpt, an open source package to simplify sequential optimization tasks that incorporate human preference feedback. Our approach extends an existing latent variable model for binary preferences to allow for observations of equivalent preference from users.
Code (1)
Similar Papers 제목 키워드 기반
Stackelberg Learning from Human Feedback: Preference Optimization as a Sequential Game
We introduce Stackelberg Learning from Human Feedback (SLHF), a new framework for preference optimization. SLHF frames the alignment problem as a sequential-move game between two policies: a Leader, which commits to an a…
Reinforcement LearningShapley-based Data Valuation for LLM Alignment via Sequential Preference Optimization
Data valuation is a natural framework for understanding which preference datasets matter most when aligning a Large Language Model (LLM) using multiple sources. The standard game-theoretic approach assigns each dataset a…
SPO: Multi-Dimensional Preference Sequential Alignment With Implicit Reward Modeling
Human preference alignment is critical in building powerful and reliable large language models (LLMs). However, current methods either ignore the multi-dimensionality of human preferences (e.g. helpfulness and harmlessne…
Sequential Learning-based IaaS Composition
We propose a novel IaaS composition framework that selects an optimal set of consumer requests according to the provider's qualitative preferences on long-term service provisions. Decision variables are included in the t…
ClusteringQ-LearningBeyond Uniform Forgetting: A Study of Sequential Direct Preference Optimization Across Preference Settings
Aligning language models with human preferences often requires optimising multiple behavioural objectives. A practical approach is to apply these objectives sequentially using preference optimisation methods such as Dire…