1 citations · 4 across the 12 of their papers we have counts for
3 papers · 1 filter
ResponseRank: Data-Efficient Reward Modeling through Preference Strength Learning
Timo Kaufmann, Yannick Metz, Daniel Keim +1
Binary choices, as often used for reinforcement learning from human feedback (RLHF), convey only the direction of a preference. A person may choose apples over oranges and bananas…
Evaluating Autoencoders for Parametric and Invertible Multidimensional Projections
Frederik L. Dennig, Nina Geyer, Daniela Blumberg +2
Recently, neural networks have gained attention for creating parametric and invertible multidimensional data projections. Parametric projections allow for embedding previously unse…
Reward Learning from Multiple Feedback Types
Yannick Metz, András Geiszl, Raphaël Baur +1
Learning rewards from preference feedback has become an important tool in the alignment of agentic models. Preference-based feedback, often implemented as a binary comparison betwe…