Feedback-driven search for generative models beyond local reward alignment
arXiv·low signal
Sequentially-Controlled Interactive Multi-Particle Flow-Maps introduces an online, feedback-driven search method for generative models that improves on training-free reward alignment, which the authors say typically excels only at narrow local exploration. Relevant to agentic optimization and search loops where a generator must be steered by ongoing reward signals. cs.LG/cs.AI/cs.CE, published July 1, 2026.