aiqbench analysis
This paper addresses the critical degradation of image editing models during iterative, multi-turn workflows by introducing MT-OPSD, a self-distillation framework that aligns training conditions with recursive inference realities. By leveraging a clean-conditioned teacher to supervise self-generated states, the method significantly improves long-horizon editing success without requiring extensive new annotations. The authors also release LME-Bench, a robust benchmark of 100 ten-turn sessions designed to standardize evaluation of long-horizon generative capabilities.
On-Policy Self-Distillation for Multi-Turn Image Editing
arXiv:2609.35611v1 Announce Type: cross Abstract: Instruction-based image editing has achieved strong performance in single-turn settings, yet practical editing is often iterative, with each instruction applied to the output of the previous turn. We find that existing editing models degrade rapidly under recursive editing and attribute this failure to a train-test mismatch in the conditioning distribution: models are trained on clean source images but must repeatedly condition on their own imperfect outputs at inference time. To address this, we propose MT-OPSD, an on-policy self-distillation framework that trains the model on self-generated conditioning states with editing supervision from a clean-conditioned teacher, without requiring multi-turn annotations. We further introduce LME-Bench, a benchmark of 100 ten-turn editing sessions for evaluating long-horizon robustness. Experiments across three editing backbones show that MT-OPSD substantially improves long-horizon editing success and reduces multi-turn collapse while largely preserving single-turn editing quality.
Abstract:Instruction-based image editing has achieved strong performance in single-turn settings, yet practical editing is often iterative, with each instruction applied to the output of the previous turn. We find that existing editing models degrade rapidly under recursive editing and attribute this failure to a train-test mismatch in the conditioning distribution: models are trained on clean source images but must repeatedly condition on their own imperfect outputs at inference time. To address this, we propose MT-OPSD, an on-policy self-distillation framework that trains the model on self-generated conditioning states with editing supervision from a clean-conditioned teacher, without requiring multi-turn annotations. We further introduce LME-Bench, a benchmark of 100 ten-turn editing sessions for evaluating long-horizon robustness. Experiments across three editing backbones show that MT-OPSD substantially improves long-horizon editing success and reduces multi-turn collapse while largely preserving single-turn editing quality.
Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
arXiv:2609.35611 [cs.CV]
(or arXiv:2609.35611v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2609.35611 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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