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cs.LG2026
Smaller Models, Better Rejects: Preference Distillation Scaling
Rui Cai, Wenhui Zhu, Xiwen Chen +15
Preference distillation typically treats a teacher response as preferred and the student's own response as rejected. This assumes that self-generated failures are the most informat…
cs.LG2024
PAL: Pluralistic Alignment Framework for Learning from Heterogeneous Preferences
Daiwei Chen, Yi Chen, Aniket Rege +1
Large foundation models pretrained on raw web-scale data are not readily deployable without additional step of extensive alignment to human preferences. Such alignment is typically…
cs.LG2023★ 2 cited
Learning Capacity: A Measure of the Effective Dimensionality of a Model
Daiwei Chen, Wei-Kai Chang, Pratik Chaudhari
We use a formal correspondence between thermodynamics and inference, where the number of samples can be thought of as the inverse temperature, to study a quantity called ``learning…