RewardSlider: Continuous Image Editing via Reward-Guided Trajectory Control

Existing instruction-based image editing models typically produce fully edited outputs, making it difficult to continuously and finely control the degree of editing. We present **RewardSlider**, a continuous image editing framework based on reward-guided trajectory control. We first observe that the editing dynamics of FLUX-Kontext exhibit pronounced spatiotemporal plasticity: early generation steps are substantially more sensitive to interventions, and this sensitivity is concentrated in instruction-relevant regions. Building on this observation, RewardSlider keeps the original editing model frozen and introduces lightweight velocity controls only at early timesteps and within edit-relevant regions. Multiple complementary reward models are combined into a terminal optimization objective to assess the generated result from different perspectives, including semantic edit satisfaction, content preservation, and visual quality. Different target strengths are associated with distinct reward constraints, and the terminal rewards are backpropagated through the complete subsequent generation dynamics to jointly optimize a shared set of early control variables, thereby guiding the model to produce outputs of varying strengths along a coherent and continuous editing trajectory. Without requiring intermediate-strength training data or additional model fine-tuning, RewardSlider turns a pretrained instruction-based image editor into a continuously adjustable, fine-grained editing system.