Stepwise Credit Assignment for GRPO on Flow-Matching Models

5 Steps

1. 1

   Analyze Generative Process Temporal Dynamics: Understand how different stages of your flow-matching or diffusion model's generation process contribute to the final output. Identify which early steps establish global structure and composition, and which later steps refine details and texture.

2. 2

   Design a Stepwise Reward Weighting Scheme: Propose a non-uniform weighting strategy for rewards. Assign higher weights to rewards obtained from steps critical for structural integrity (early stages) or detail fidelity (later stages), based on your analysis in Step 1. This moves beyond uniform credit assignment.

3. 3

   Modify Your GRPO Reward Function: Integrate the designed stepwise weights into your GRPO (or equivalent Reinforcement Learning from Human Feedback - RLHF) reward function. The reward signal for each generation step should be multiplied by its corresponding weight.

4. 4

   Implement and Retrain Your Model: Apply the modified, temporally-aware reward function during the training of your flow-matching generative model. Ensure your training loop correctly applies the per-step weights when calculating the policy gradient.

5. 5

   Evaluate Performance Against Baseline: Compare the performance of your model trained with stepwise credit assignment against a baseline using uniform credit assignment. Assess improvements in sample quality, training stability, convergence speed, and the model's ability to generate both coherent structure and fine details.