Latest Flow Matching Research Papers
The newest Flow Matching papers from across the field — arXiv, NeurIPS, CVPR, Nature, and more — refreshed daily and ranked by relevance. Distill AI tracks Flow Matching so you don’t have to: get the standout work delivered to your inbox every morning, with 2-sentence summaries and the option to chat with any paper.
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- OncoTraj: a public benchmark for longitudinal resistance prediction in EGFR-mutant non-small-cell lung cancer on osimertinibAbhijoy Sarkar, Aarchi Singh Thakur · arXiv · Jun 9, 2026
Resistance to first-line osimertinib in EGFR-mutant non-small-cell lung cancer (NSCLC) is the canonical example of predictable clonal evolution under therapeutic pressure, yet no public benchmark exists for training or evaluating computatio…
- Learning Manifold Data with Flow MatchingSophia Pi, Mingcheng Lu, Jerry Yao-Chieh Hu, Maojiang Su et al. · SPIGM @ ICML Poster · May 30, 2026
We study flow-matching transformers when data lie on a low-dimensional manifold. Our key insight is a flow decomposition that splits motion along the manifold from motion off the manifold. The scheme works for first- and higher-order flow m…
- Learning Manifold Data with Flow MatchingSophia Pi, Mingcheng Lu, Jerry Yao-Chieh Hu, Maojiang Su et al. · ICML 2026 FoGen Workshop Poster · May 26, 2026
We study flow-matching transformers when data lie on a low-dimensional manifold. Our key insight is a flow decomposition that splits motion along the manifold from motion off the manifold. The scheme works for first- and higher-order flow m…
- Flow Sampling: Learning to Sample from Unnormalized Densities via Denoising Conditional ProcessesAaron Havens, Brian Karrer, Neta Shaul · arXiv · May 5, 2026
Sampling from unnormalized densities is analogous to the generative modeling problem, but the target distribution is defined by a known energy function instead of data samples. Because evaluating the energy function is often costly, a prima…
- Turning the TIDE: Cross-Architecture Distillation for Diffusion Large Language ModelsGongbo Zhang, Wen Wang, Ye Tian, Li Yuan · arXiv · Apr 29, 2026
Diffusion large language models (dLLMs) offer parallel decoding and bidirectional context, but state-of-the-art dLLMs require billions of parameters for competitive performance. While existing distillation methods for dLLMs reduce inference…
- Low-Pass Flow MatchingFrancesco M. Ruscio, T. Konstantin Rusch · ICLR 2026 DeLTa Workshop Poster · Mar 3, 2026
Flow Matching typically relies on white noise sources, a choice often misaligned with the power spectra of natural data, which tend to decay with frequency. To address this, we introduce $\textbf{Low-Pass Flow Matching}$, a variant of Flow …
- Flow Matching Policy GradientsDavid McAllister, Songwei Ge, Brent Yi, Chung Min Kim et al. · ICLR 2026 Poster · Jan 26, 2026
Flow-based generative models, including diffusion models, excel at modeling continuous distributions in high-dimensional spaces. In this work, we introduce Flow Policy Optimization (FPO), a simple on-policy reinforcement learning algorithm …
- Topological Flow MatchingKacper Wyrwal, Ismail Ilkan Ceylan, Alexander Tong · ICLR 2026 Poster · Jan 26, 2026
Flow matching is a powerful generative modeling framework, valued for its simplicity and strong empirical performance. However, its standard formulation treats signals on structured spaces---such as fMRI data on brain graphs---as points in …
- Active Flow MatchingYashvir Singh Grewal, Edwin V. Bonilla, Thang D Bui · AIML-CEB 2025 Oral · Nov 12, 2025
Discrete diffusion and flow matching excel at capturing epistatic structure in protein fitness landscapes through parallel, iterative refinement. However, their implicit nature—sampling via learned dynamics without tractable densities—preve…
- Federated Flow MatchingZifan Wang, Anqi Dong, Mahmoud Selim, Michael M. Zavlanos et al. · Submitted to ICLR 2026 · Sep 19, 2025
Data today is decentralized, generated and stored across devices and institutions where privacy, ownership, and regulation prevent centralization. This motivates the need to train generative models directly from distributed data locally wit…
- ReinFlow: Fine-tuning Flow Matching Policy with Online Reinforcement LearningTonghe Zhang, Chao Yu, Sichang Su, Yu Wang · Online Reinforcement Learning, Flow Matching Policy, Fine-tuning, Robot Learning · May 11, 2025
We propose ReinFlow, a simple yet effective online reinforcement learning (RL) framework that fine-tunes a family of flow matching policies for continuous robotic control. Derived from rigorous RL theory, ReinFlow injects learnable noise in…
- Variational Rectified Flow MatchingPengsheng Guo, Alex Schwing · ICML 2025 poster · May 1, 2025
We study Variational Rectified Flow Matching, a framework that enhances classic rectified flow matching by modeling multi-modal velocity vector-fields. At inference time, classic rectified flow matching 'moves' samples from a source distrib…
- Variational Rectified Flow MatchingPengsheng Guo, Alex Schwing · ICLR 2025 DeLTa Workshop Poster · Mar 6, 2025
We study Variational Rectified Flow Matching, a framework that enhances classic rectified flow matching by modeling multi-modal velocity vector-fields. At inference time, classic rectified flow matching 'moves' samples from a source distrib…
- Robot Manipulation with Flow MatchingFan Zhang, Michael Gienger · CoRL 2024 Workshop MRM-D Poster · Oct 29, 2024
This paper presents a new imitation learning paradigm for robot manipulation with flow matching policy. Flow matching represents a robot visuomotor policy as a conditional process of flowing random waypoints to desired robot action trajecto…
- One-step Flow Matching GeneratorsZemin Huang, Zhengyang Geng, Weijian Luo, Guo-Jun Qi · Submitted to ICLR 2025 · Sep 27, 2024
In the realm of Artificial Intelligence Generated Content (AIGC), flow-matching models have emerged as a powerhouse, achieving success due to their robust theoretical underpinnings and solid ability for large-scale generative modeling. Thes…
- Discrete Flow MatchingItai Gat, Tal Remez, Neta Shaul, Felix Kreuk et al. · NeurIPS 2024 spotlight · Sep 25, 2024
Despite Flow Matching and diffusion models having emerged as powerful generative paradigms for continuous variables such as images and videos, their application to high-dimensional discrete data, such as language, is still limited. In this…
- Preference Alignment with Flow MatchingMinu Kim, Yongsik Lee, Sehyeok Kang, Jihwan Oh et al. · NeurIPS 2024 poster · Sep 25, 2024
We present Preference Flow Matching (PFM), a new framework for preference alignment that streamlines the integration of preferences into an arbitrary class of pre-trained models. Existing alignment methods require fine-tuning pre-trained mo…
- Variational Rectified Flow MatchingPengsheng Guo, Alex Schwing · Submitted to ICLR 2025 · Sep 20, 2024
We study Variational Rectified Flow Matching, a framework that enhances classic rectified flow matching by modeling multi-modal velocity vector-fields. At inference time, classic rectified flow matching 'moves' samples from a source distrib…