Title: BranchGRPO: Stable and Efficient GRPO with Structured Branching in Diffusion Models
Primary area: generative models
Keywords: Diffusion Models, Flow Matching, RLHF, GRPO, Efficient Training

Abstract: Recent progress in aligning image and video generative models with Group Relative Policy Optimization (GRPO) has improved human preference alignment, but existing variants remain inefficient due to sequential rollouts and large numbers of sampling steps, unreliable credit assignment,as sparse terminal rewards are uniformly propagated across timesteps, failing to capture the varying criticality of decisions during denoising.
In this paper, we present BranchGRPO, a method that restructures the rollout process into a branching tree, where shared prefixes amortize computation and pruning removes low-value paths and redundant depths.  
BranchGRPO introduces three contributions: 
(1) a branching scheme that amortizes rollout cost through shared prefixes while preserving exploration diversity; 
(2) a reward fusion and depth-wise advantage estimator that transforms sparse terminal rewards into dense step-level signals; and 
(3) pruning strategies that cut gradient computation but leave forward rollouts and exploration unaffected.  
On HPSv2.1 image alignment, BranchGRPO improves alignment scores by up to \textbf{16\%} over DanceGRPO, while reducing per-iteration training time by nearly \textbf{55\%}.  
A hybrid variant, BranchGRPO-Mix, further accelerates training to 4.7× faster than DanceGRPO without degrading alignment.
On WanX video generation, it further achieves higher motion quality reward with sharper and temporally consistent frames.

TL;DR: We introduce BranchGRPO, a tree-structured rollout framework that makes RLHF for diffusion models more efficient and stable.