Visual RLHF Evaluation
for Image Generation Models.
Structured human preference annotation for training reward models on visual AI systems. Pairwise ranking, quality rating, and safety classification — with rubric consistency monitoring across every batch.
What Is Visual RLHF Evaluation?
Reinforcement Learning from Human Feedback (RLHF) is the process of training a reward model on human preference judgments — then using that reward model to fine-tune an AI system toward outputs that humans prefer. For image generation models, this means collecting human judgments about which AI-generated images are better, and why.
Visual RLHF evaluation requires annotators to make structured judgment calls about image quality — not just pick a favourite, but evaluate specific dimensions such as prompt adherence, visual coherence, aesthetic quality, and safety. The consistency of these judgments directly determines the quality of the reward model signal.
Aeterna Data approaches visual RLHF evaluation as a precision annotation task, not a subjective rating exercise. Structured rubrics define exactly what each rating dimension means. Rubric consistency is monitored across batches to detect and correct annotator drift before it affects the reward model.
Task Types
Pairwise Preference Ranking
Annotators are shown two AI-generated images produced from the same prompt and select the preferred image on each evaluation dimension. The pairwise format reduces the complexity of the judgment — comparing two options is more reliable than assigning an absolute score.
Prompt adherence · Visual coherence · Aesthetic quality · Safety
Absolute Quality Rating
Annotators assign a score to a single image on a defined scale for each evaluation dimension. Used when pairwise comparison is not practical — for example, when the dataset is too large for all-pairs comparison or when absolute thresholds need to be established.
Per-dimension 1-5 scale · Rubric-anchored · Documented examples
Safety Classification
Annotators classify AI-generated images against defined safety categories — for example, whether an image contains harmful content, follows platform guidelines, or meets content policy requirements.
Binary safe/unsafe · Multi-category policy classification · Severity rating
Use Cases
Text-to-Image Models
Preference data for reward model training on image generation systems such as diffusion models. Evaluating prompt adherence, visual quality, and safety across generated outputs.
Image Editing Models
Evaluation of AI-powered image editing — comparing edited outputs against the original and the editing instruction for accuracy and quality.
Image-to-Image Translation
Quality evaluation for style transfer, super-resolution, and domain adaptation models — comparing outputs against ground truth or reference images.
Content Policy Compliance
Safety classification for image generation systems — identifying outputs that violate content policies before they reach end users.
Rubric Design
The quality of RLHF preference data depends almost entirely on how well the evaluation rubric is designed. A vague rubric produces inconsistent judgments. An inconsistent dataset produces a noisy reward model. A noisy reward model produces a poorly aligned AI system.
Aeterna Data works with clients to define or refine the evaluation rubric before the pilot begins. Each dimension is given a precise definition, a scale, and annotated examples of what each scale point looks like in practice. This is done before a single image is evaluated.
Dimension Decomposition
Complex quality judgments are broken into specific, independently evaluable dimensions. 'Good image' is not a rubric dimension. 'Prompt adherence — does the image contain all the elements described in the prompt' is a rubric dimension.
Scale Anchoring
Every point on the rating scale is anchored with a written definition and a visual example. Annotators do not interpret the scale — they apply it. This is the primary mechanism for reducing rubric drift across annotators and batches.
Drift Monitoring
Rubric consistency is monitored across production batches. If IAA on a specific dimension drops below threshold, the dimension is reviewed, the rubric is clarified, and the affected batch is reworked.
Quality Standard
Every visual RLHF evaluation batch delivered by Aeterna Data is measured for inter-annotator agreement before delivery. IAA is not a target — it is a threshold. Batches that do not meet the threshold are reworked before the client receives them.
Cohen's Kappa
Pairwise IAA between two annotators on the same image set. Applied on every batch.
Fleiss' Kappa
Multi-annotator IAA across three or more annotators. Applied on complex multi-class tasks.
Every batch delivery includes a quality report: IAA scores by class, box count, annotator distribution, and any deviation notes. No dataset delivered without the quality documentation.
Our Workflow
Project Brief & Scoping
You share your dataset sample, object classes, and output format requirements. We scope the pilot — sample size, timeline, and deliverables — and confirm before work begins.
Pilot Phase
We evaluate a representative sample of your image pairs using the agreed rubric. IAA is measured per dimension. Any dimension with low agreement is reviewed and the rubric is clarified before production begins.
Production Annotation
Full dataset annotation following the validated workflow. IAA measured on every batch. Rework at no cost for any batch below threshold.
Delivery & Documentation
Annotated dataset delivered in your specified format with a complete quality report — IAA scores, class distribution, and annotator notes.
Annotation Tools
Aeterna Data works with the annotation platform your team already uses. We do not require you to adopt a specific tool. If you do not have a platform, we can advise on setup based on your task type and scale.
How to Start
Send a Project Brief
Share your image generation model outputs, evaluation dimensions, and any existing rubric or content policy. We will review and propose a pilot scope within 48 hours.
Send BriefReceive a Pilot Proposal
We send a scoped pilot proposal within 48 hours — sample size, timeline, flat-rate pilot fee, and deliverables.
NDA and DPA Signed
Before any data is shared, NDA and Data Processing Agreement are signed. Your dataset stays confidential.
Pilot Begins
Annotation starts inside your platform. IAA report delivered with the pilot dataset. Production follows on your confirmation.
Ready to Build Your Visual RLHF Dataset?
Rubric designed and validated in the pilot. IAA measured per dimension. Consistent preference signal on every batch.