A Test-time Actor-Critic Approach to News Images Generation
On the map: Deep reinforcement learning methods → RL with diffusion and flow models
Abstract (arXiv)
This paper introduces the CERTH-ITI solution for the MediaEval NewsImages 2026 challenge, which focuses on generating images related to news headlines. Inspired by the Actor-Critic paradigm in reinforcement learning, we present a test-time, model-agnostic Actor-Critic Image Generation approach (ACIG). ACIG generates prompts for image creation, produces the images, evaluates the generated results, and if needed refines the image generation prompts accordingly in a feedback loop. ACIG achieved the best results in the NewsImages 2026 challenge, according to the challenge's leaderboard.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL with diffusion and flow models, one of 15 directions in Deep reinforcement learning methods. Builds on 6 of its 12 references that are in the map. First posted on arXiv 19 Jun 2026.
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- 2025Z-Image: An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformercited by 277
- 2023Prompting AI Art: An Investigation into the Creative Skill of Prompt Engineeringcited by 43
- 2023PACE: Improving Prompt with Actor-Critic Editing for Large Language Modelcited by 20
- 2025GenPilot: A Multi-Agent System for Test-Time Prompt Optimization in Image Generationcited by 6
- 2026Improving Calibration in Test-Time Prompt Tuning for Vision-Language Models via Data-Free Flatness-Aware Prompt Pretrainingcited by 2
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