Rethinking Test Time Scaling for Flow-Matching Generative Models

ArXi:2511.22242v3 Announce Type: replace The performance of text-to-image diffusion models may be improved at test-time by scaling computation to search for a generated image that maximizes a given reward function. While existing trajectory level exploration methods improve the effectiveness of test-time scaling for standard diffusion models, they are largely incompatible with modern flow matching models, which use deterministic sampling. This imposes significant computational overhead on local trajectory search, making the trade-offs less favorable compared to global search.