End-to-End RL for Diffusion Models Resurfaces in Academic Debate
Kangwook_Lee · x · 2026-08-02
In a discussion on how generative models solve mode-averaging via step-by-step generation (like autoregressive token prediction or diffusion denoising), researcher @AlexiGlad pointed out that this creates a train-inference mismatch, leading to compounding errors or exposure bias.
Addressing the trend towards end-to-end (e2e) training for generative models, UW-Madison scholar @KangwookLee noted that researcher @yingfanbot actually implemented end-to-end reinforcement learning (e2e RL) for diffusion models many years ago, providing a crucial historical reference for current explorations.
Related event: Policy Gradient in Diffusion Models Sparks Debate(3 posts)→
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