Flash-GRPO: Efficient Alignment for Video Diffusion via One-Step Policy Optimization
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| Authors | Xiaoxuan He et al. |
| Year | 2026 |
| HF Upvotes | 5 |
| arXiv | 2605.15980 |
| Download | |
| Code | https://github.com/Shredded-Pork/Flash-GRPO |
Abstract
Group Relative Policy Optimization has emerged as essential for aligning video diffusion models with human preferences, but faces a critical computational bottleneck: training a 14B parametered model typically demands hundreds of GPU days per experiment. Existing efficiency methods reduce costs through sliding window subsampling training timesteps, but fundamentally compromise optimization, exhibiting severe instability and failing to reach full trajectory performance. We present Flash-GRPO, a single-step training framework that outperforms full trajectory training in alignment quality under low computational budgets while substantially improving training efficiency. Flash-GRPO addresses two critical challenges: iso-temporal grouping eliminates timestep-confounded variance by enforcing prompt-wise temporal consistency, decoupling policy performance from timestep difficulty; temporal gradient rectification neutralizes the time-dependent scaling factor that causes vastly inconsistent gradient magnitudes across timesteps. Experiments on 1.3B to 14B parameter models validate Flash-GRPO's effectiveness, demonstrating substantial training acceleration with consistent stability and state-of-the-art alignment quality.
Engineering Breakdown
Plain English
Flash-GRPO cuts the training cost of aligning 14B video diffusion models from hundreds of GPU days down to a single training step, while actually improving alignment quality compared to full trajectory training. The key breakthrough is a technique called iso-temporal grouping that removes variance caused by different timesteps, letting the model learn effectively from just one optimization pass instead of sliding through multiple timesteps.
Key Engineering Insight
Single-step optimization can outperform multi-step training when you properly decouple timestep effects from policy learning signals—this challenges the conventional wisdom that alignment requires iterating through full trajectory sequences.
Why It Matters for Engineers
Video diffusion alignment is computationally prohibitive today (hundreds of GPU days per experiment limits iteration and experimentation). Flash-GRPO makes alignment practical by reducing training to hours, which directly enables faster iteration on preference tuning, better product release cycles, and more engineers able to run alignment experiments without massive compute budgets.
Research Context
Prior work used sliding window subsampling to speed up GRPO training but hit a wall—those methods were unstable and never reached full performance. Flash-GRPO solves the fundamental problem by redesigning what gets optimized in each step rather than just training less, representing a shift from 'optimize harder' to 'optimize smarter' in the alignment space.
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