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#Self-distillation

Internalizing Meta-Experience into Memory for Guided Reinforcement Learning in Large Language Models

Intermediate

Shiting Huang, Zecheng Li et al.Feb 10arXiv

The paper teaches large language models to do what good students do: find where they went wrong, turn that lesson into a rule, and remember it for next time.

#Reinforcement Learning with Verifiable Rewards#RLVR#Meta-Experience Learning

Self-Evaluation Unlocks Any-Step Text-to-Image Generation

Intermediate

Xin Yu, Xiaojuan Qi et al.Dec 26arXiv

This paper introduces Self-E, a text-to-image model that learns from scratch and can generate good pictures in any number of steps, from just a few to many.

#Self-Evaluating Model#Any-step inference#Text-to-image generation

Visual Generation Tuning

Intermediate

Jiahao Guo, Sinan Du et al.Nov 28arXiv

Before this work, big vision-language models (VLMs) were great at understanding pictures and words together but not at making new pictures.

#Visual Generation Tuning#VGT-AE#Vision-Language Models

VQRAE: Representation Quantization Autoencoders for Multimodal Understanding, Generation and Reconstruction

Intermediate

Sinan Du, Jiahao Guo et al.Nov 28arXiv

VQRAE is a new kind of image tokenizer that lets one model both understand images (continuous features) and generate/reconstruct them (discrete tokens).

#VQRAE#Vector Quantization#Representation Autoencoder