Principle:Alibaba ROLL Reward Flow Configuration
| Knowledge Sources | |
|---|---|
| Domains | Diffusion_Models, Configuration, Reinforcement_Learning |
| Last Updated | 2026-02-07 20:00 GMT |
Overview
A configuration principle for setting up reward flow-based RL training of video diffusion models with LoRA fine-tuning.
Description
Reward Flow Configuration manages the parameters for training video diffusion models (Wan2.2) using reward-based optimization. Unlike LLM training pipelines, this configuration specifies diffusion-specific settings including model component paths, LoRA rank/target modules, Euler scheduler parameters, and DeepSpeed-based training with diffusion-specific strategy.
Usage
Use when setting up a reward flow pipeline for video diffusion model fine-tuning with face identity preservation or other reward objectives.
Theoretical Basis
Reward flow optimizes diffusion model parameters through the denoising process, backpropagating reward signals through the Euler ODE solver.
Related Pages
Implemented By
Related Heuristics
No specific heuristics inform this principle.