Sampling Meets Interaction: Sequentially-Controlled Multi-Particle Flow-Maps for Efficient Inference-time Search
While generative models have enabled training-free reward alignment, existing particle-based methods are fundamentally constrained by their propensity to local exploration within narrow regions of the underlying distribution, severely restricting sample diversity. This limitation becomes especially acute under tight reward-feedback budgets, where effective search demands broad, strategic exploration to uncover high-utility regions. To address this, we propose Sequentially-Controlled Interactive Multi-Particle Flow-Maps (IMPFM), a framework for feedback-efficient search. IMPFM progressively transports a group of interactive particles toward the target distribution, maintaining the broad co...
| Authors | Binglin Ji, Anindya Sarkar, Hengchang Lu et al. |
|---|---|
| Primary category | cs.LG |
| Published | 1 July 2026 |
| Last updated at source | 28 September 2026 |
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