WarmPrior: Straightening Flow-Matching Policies with Temporal Priors
Authors: Sinjae Kang, Chanyoung Kim, Kaixin Wang et al.
Summary
arXiv:2605. 13959v1 Announce Type: new Abstract: Generative policies based on diffusion and flow matching have become a dominant paradigm for visuomotor robotic control.
Relevance
Read next because WarmPrior: Straightening Flow-Matching Policies with Temporal Priors overlaps with clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)", clean result "Training one persona to emit a [ZLT] marker without bystanders adopting it has a one-cell-wide LR x epochs window on Qwen2.5-7B-Instruct (LOW confidence)", clean result "A pretraining-data-poisoned Qwen3-4B backdoor only fires on the exact trigger tokens — paraphrases don't activate it, and base-model similarity to the trigger doesn't predict which inputs fire (MODERATE confidence)". Matching terms: rect, under, source, rate, control, lora. Source: arxiv cs.LG (Machine Learning).
Abstract
arXiv:2605.13959v1 Announce Type: new Abstract: Generative policies based on diffusion and flow matching have become a dominant paradigm for visuomotor robotic control. We show that replacing the standard Gaussian source distribution with WarmPrior, a simple temporally grounded prior constructed from readily available recent action history, consistently improves success rates on robotic manipulation tasks. We trace this gain to markedly straighter probability paths, echoing the effect of optimal-transport couplings in Rectified Flow. Beyond standard behavior cloning, WarmPrior also reshapes the exploration distribution in prior-space reinforcement learning, improving both sample efficiency and final performance. Collectively, these results identify the source distribution as an important and underexplored design axis in generative robot control.