From Plans to Pixels: Learning to Plan and Orchestrate for Open-Ended Image Editing

2026-05-14 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

Anirudh Sundara Rajan, Krishna Kumar Singh, and Yong Jae Lee introduce an experiential framework for long-horizon image editing, addressing the limitations of current models in handling abstract, multi-step instructions like "make this advertisement more vegetarian-friendly." Their approach, detailed in paper 2605.15181, integrates a planner that generates structured atomic task decompositions and an orchestrator that selects appropriate tools and image regions for execution. A vision-language judge provides outcome-based rewards, evaluating instruction adherence and visual quality. The orchestrator is trained to maximize these rewards, and successful execution trajectories are then used to refine the planner, creating a tightly coupled system that outperforms single-step or rule-based multi-step baselines in producing coherent and reliable edits.

Key takeaway

For research scientists developing advanced image editing systems, this framework offers a robust method for tackling abstract, multi-step instructions. By integrating a reward-driven orchestrator with a refining planner, you can achieve more coherent and reliable edits than traditional single-step or rule-based approaches. Consider adopting this experiential learning paradigm to enhance the flexibility and performance of your next-generation image manipulation tools.

Key insights

A new framework couples planning with reward-driven execution for complex, multi-step image editing tasks.

Principles

• Decompose complex tasks into atomic steps.
• Reward-based learning improves execution and planning.

Method

A planner generates task decompositions, an orchestrator executes steps with tools/regions, and a vision-language judge provides rewards to refine both components through experiential learning.

In practice

• Apply structured decomposition to abstract editing.
• Use vision-language models for outcome evaluation.

Topics

• Open-Ended Image Editing
• Long-Horizon Planning
• Experiential Learning
• Vision-Language Judge
• Reward-Driven Execution

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 Takara TLDR - Daily AI Papers.