Edit-Compass & EditReward-Compass: A Unified Benchmark for Image Editing and Reward Modeling

TL;DR

This paper introduces Edit-Compass and EditReward-Compass, comprehensive benchmarks for evaluating image editing and reward modeling, addressing limitations of existing benchmarks with more challenging tasks and realistic evaluation protocols.

Contribution

The authors present a unified evaluation suite with extensive annotated instances and multidimensional scoring for image editing and reward modeling, improving assessment fidelity.

Findings

Contains 2,388 annotated instances across six challenging categories.

Includes 2,251 preference pairs for realistic reward modeling evaluation.

Employs a fine-grained, structured evaluation framework.

Abstract

Recent image editing models have achieved remarkable progress in instruction following, multimodal understanding, and complex visual editing. However, existing benchmarks often fail to faithfully reflect human judgment, especially for strong frontier models, due to limited task difficulty and coarse-grained evaluation protocols. In parallel, reward models have become increasingly important for RL-based image editing optimization, yet existing reward model benchmarks still rely on unrealistic evaluation settings that deviate from practical RL scenarios. These limitations hinder reliable assessment of both image editing models and reward models. To address these challenges, we introduce Edit-Compass and EditReward-Compass, a unified evaluation suite for image editing and reward modeling. Edit-Compass contains 2,388 carefully annotated instances spanning six progressively challenging task categories, covering capabilities such as world knowledge reasoning, visual reasoning, and multi-image editing. Beyond broad task coverage, Edit-Compass adopts a fine-grained multidimensional evaluation framework based on structured reasoning and carefully designed scoring rubrics. In parallel, EditReward-Compass contains 2,251 preference pairs that simulate realistic reward modeling scenarios during RL optimization.