Designing Instance-Level Sampling Schedules via REINFORCE with James-Stein Shrinkage

• URL: http://arxiv.org/abs/2511.22177v1
• Date: Thu, 27 Nov 2025 07:33:05 GMT
• Title: Designing Instance-Level Sampling Schedules via REINFORCE with James-Stein Shrinkage
• Abstract summary: We reschedule the sampling timeline of a frozen text-to-image sampler.<n>We introduce a novel reward baseline based on a principled James-Stein estimator.<n>We position our scheduling framework as an emerging model-agnostic post-training lever.
• Score: 46.743961849406844
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Most post-training methods for text-to-image samplers focus on model weights: either fine-tuning the backbone for alignment or distilling it for few-step efficiency. We take a different route: rescheduling the sampling timeline of a frozen sampler. Instead of a fixed, global schedule, we learn instance-level (prompt- and noise-conditioned) schedules through a single-pass Dirichlet policy. To ensure accurate gradient estimates in high-dimensional policy learning, we introduce a novel reward baseline based on a principled James-Stein estimator; it provably achieves lower estimation errors than commonly used variants and leads to superior performance. Our rescheduled samplers consistently improve text-image alignment including text rendering and compositional control across modern Stable Diffusion and Flux model families. Additionally, a 5-step Flux-Dev sampler with our schedules can attain generation quality comparable to deliberately distilled samplers like Flux-Schnell. We thus position our scheduling framework as an emerging model-agnostic post-training lever that unlocks additional generative potential in pretrained samplers.

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