Papers

Filtered to guidance · clear filter

Browse by term

continual learning 83reinforcement learning 50large language models 12benchmarking 11benchmarks 11language models 11vision-language models 11robotics 7natural language processing 6world models 6generative models 5recursive self-improvement 5attention mechanisms 4diffusion Transformers 4multi-agent systems 4multimodal learning 4multimodal models 4on-policy distillation 4self-distillation 4self-supervised learning 4transformers 4video generation 4vision-language-action models 4agent-based systems 3agentic models 3agentic search 3autonomous systems 3coding agents 3diffusion models 3image generation 3

Matching papers

Rethinking Classifier-Free Guidance in On-Policy Diffusion Distillation

70 upvotes · 27 JUL 2026 · Bingnan Li, Haozhe Wang, Haozhong Xiong et al.

This paper investigates how to improve the adaptation of diffusion models in a way that doesn't rely on a classifier, and how to address a problem where the model can't accurately learn from its teacher. Practitioners might care about this because it could lead to more effective knowledge transfer in machine learning applications.