DiffusionRVGE: Value Gradient Estimation via Regression for Reinforcement Learning in Diffusion Models
In brief
- DiffusionRVGE optimizes diffusion models via reinforcement learning without expensive backpropagation through sampling.
- Cuts training compute by 80% while outperforming a baseline on 7 of 8 tested metrics.
- Adaptive rollout strategies based on reward continuity improve efficiency and performance.
DiffusionRVGE introduces a reinforcement learning method for diffusion models that avoids costly reward derivatives and backpropagation through sampling trajectories. The approach reduces training compute by up to 80% while matching or outperforming a baseline on multiple benchmarks. Results show significant efficiency gains without sacrificing performance.
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