Research
TailSteer: Trajectory-Tail Steering for Drift-Resistant Multi-turn Image Editing
TailSteer is a final-stage training framework that improves multi-turn image editing robustness by concentrating preservation corrections near the trajectory tail of the denoising process. It learns from the editor's own on-policy rollouts using unchanged source content as reference, avoiding curated ground-truth pairs. Experiments on FLUX, SenseNova, and Qwen families show reduced cumulative drift over 15-20 turns while preserving base-model performance.
image editingmulti-turn robustnessfoundation models