Scaling Multi-Reference Image Generation with Dynamic Reward Optimization

2026-06-25 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision & Pattern Recognition · Depth: Expert, quick

Summary

A new research introduces OmniRef-Bench, a benchmark designed to evaluate multi-reference image generation (MRIG) models in complex scenarios involving diverse reference image types and large quantities. Evaluations using OmniRef-Bench reveal that current mainstream open-source models perform poorly in these complex MRIG tasks, with performance significantly degrading as the number of mixed-type reference images increases. To address this limitation, the authors propose DyRef, a two-stage training framework. The first stage employs supervised fine-tuning to establish foundational capabilities for complex MRIG. The second stage integrates Difficulty-aware Advantage Reweighting (DAR) for dynamic optimization adjustment and Discriminative Reward Scaling (DRS) to enhance policy optimization by increasing intra-group reward differences. Experiments confirm that DyRef substantially improves open-source model performance on both OmniRef-Bench and single-image editing benchmarks, demonstrating its effectiveness and generalization.

Key takeaway

For Machine Learning Engineers developing multi-reference image generation systems, you should consider adopting the DyRef two-stage training framework. This approach significantly enhances model performance in complex scenarios involving numerous and diverse reference images, a known weakness of current open-source models. Evaluate your models against the new OmniRef-Bench to accurately assess their capabilities and identify areas for improvement, especially when scaling reference inputs.

Key insights

DyRef, a two-stage framework, significantly improves multi-reference image generation by addressing complex scenarios and scaling challenges.

Principles

• Existing benchmarks fail to evaluate complex MRIG scenarios.
• Model performance degrades with more mixed-type reference images.
• Dynamic reward adjustment improves complex MRIG tasks.

Method

DyRef is a two-stage framework: supervised fine-tuning, then Difficulty-aware Advantage Reweighting (DAR) and Discriminative Reward Scaling (DRS) for policy optimization.

In practice

• Evaluate MRIG models using the OmniRef-Bench benchmark.
• Apply the DyRef framework to enhance open-source models.
• Utilize DAR for dynamic optimization in complex tasks.

Topics

• Multi-Reference Image Generation
• DyRef Framework
• OmniRef-Bench
• Reward Optimization
• Policy Optimization
• Computer Vision

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.