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#mode collapse

Diversity-Preserved Distribution Matching Distillation for Fast Visual Synthesis

Tianhe Wu, Ruibin Li et al.Feb 3arXiv

The paper solves a big problem in fast image generators: they got quick, but they lost variety and kept making similar pictures.

#diffusion distillation#distribution matching distillation#mode collapse

PACEvolve: Enabling Long-Horizon Progress-Aware Consistent Evolution

Minghao Yan, Bo Peng et al.Jan 15arXiv

PACEvolve is a new recipe that helps AI agents improve their ideas step by step over long periods without getting stuck.

#evolutionary search#LLM agents#context management

An Empirical Study on Preference Tuning Generalization and Diversity Under Domain Shift

Constantinos Karouzos, Xingwei Tan et al.Jan 9arXiv

Preference tuning teaches language models to act the way people like, but those habits can fall apart when the topic or style changes (domain shift).

#preference tuning#domain shift#supervised fine-tuning

GARDO: Reinforcing Diffusion Models without Reward Hacking

Haoran He, Yuxiao Ye et al.Dec 30arXiv

GARDO is a new way to fine-tune text-to-image diffusion models with reinforcement learning without getting tricked by bad reward signals.

#GARDO#reward hacking#gated KL regularization

VOYAGER: A Training Free Approach for Generating Diverse Datasets using LLMs

Avinash Amballa, Yashas Malur Saidutta et al.Dec 12arXiv

VOYAGER is a training-free way to make large language models (LLMs) create data that is truly different, not just slightly reworded.

#VOYAGER#determinantal point process#dataset diversity