TailSteer：一种用于抗漂移多轮图像编辑的轨迹尾引导训练框架
TailSteer: Trajectory-Tail Steering for Drift-Resistant Multi-turn Image Editing
TailSteer 通过保持早期去噪动态接近基础模型并从策略内滚动学习轨迹尾部的精细偏差抑制校正，显著减少多轮编辑中的累积视觉漂移，同时保留预训练的生成和编辑能力。实验表明，TailSteer 在 FLUX、SenseNova 和 Qwen 系列模型上，能在 15-20 轮编辑中大幅降低漂移，且在大量文本到图像和单图像编辑基准测试上基本保持基础模型性能。
Abstract
TailSteer, a final-stage training framework, reduces cumulative visual drift in multi-turn editing by keeping early denoising dynamics close to the base model and learning tail-focused preservation corrections from on-policy rollouts, preserving pretrained generative and editing abilities.
Strong single-turn editing performance does not guarantee reliable multi-turn interaction: as edited outputs are repeatedly reused, small unintended changes can accumulate into severe visual drift. This long-term drift is often mitigated through training on multi-turn data or cross-turn rollouts, potentially altering pretrained priors, or through specialized inference-time mechanisms that are difficult to scale to foundation models. We introduce TailSteer, a final-stage training framework that improves multi-turn robustness while retaining the generative and editing capabilities acquired through earlier training. TailSteer follows a simple principle: keep early denoising dynamics close to those of the base model and concentrate preservation corrections near the trajectory tail, where fine-grained deviations can be suppressed. These corrections are learned from the editor's own on-policy denoising rollouts, using unchanged source content as a preservation reference without requiring curated ground-truth editing pairs. Experiments across the FLUX, SenseNova, and Qwen families show that TailSteer substantially reduces cumulative drift over 15-20 editing turns while largely preserving base-model performance on extensive text-to-image and single-image editing benchmarks. These results demonstrate that long-horizon editing robustness can be unlocked in foundation models after TailSteer, rather than by relearning their existing capabilities.
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来源: Hugging Face Daily Papers · huggingface.co