Live Human Feedback in the Training Loop: Aligning Diffusion Models with Real People

Rapidata presents a method for incorporating real-time human preference feedback into the post-training of image and video diffusion models, aiming to avoid limitations of offline preference datasets and learned reward models, including divergence from human judgment and reward hacking. Using Flow-GRPO as an example, the approach replaces automated scoring of groups of generated images with crowd-sourced pairwise comparisons collected through Rapidata Flows, which converts votes into Elo-style scores using a Bradley-Terry model and feeds normalized group-relative advantages back into the training loop. The system is designed to evaluate many image groups asynchronously to reduce GPU idle time, with options to provide prompts as evaluation context and configure response targets and deadlines. The article also discusses practical considerations including LoRA-based fine-tuning, pipelining generation and evaluation, estimated annotation throughput for large GPU deployments, and centralized authentication for distributed training, while noting that the same feedback mechanism could be adapted to other online preference-optimization algorithms.

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