flow matching

topic8 events

papersTODAY 04:00 UTC

Refinement-Based Flow Policy Optimization for Online Reinforcement Learning

A new arXiv paper introduces Refinement-based Flow Policy Optimization, a method for using flow-based policies in online reinforcement learning. Standard flow matching needs samples from the target distribution, which is unavailable when the desired action distribution is only implicitly defined. The approach is presented as a refinement procedure that sidesteps this requirement in online RL settings.

reinforcement-learning/flow-matching/policy-optimization/arxiv

papersTODAY 04:00 UTC

VGFM Method Adds Dense Value Guidance to Flow Matching for Robot Policies

A new arXiv preprint introduces VGFM, a technique that guides flow-matching generative models with dense value signals to produce more expressive robot control policies. The approach targets robot learning from large offline datasets, where multimodal action representations are needed to capture varied behaviors. It aims to improve policy expressiveness within this offline learning paradigm.

robotics/flow-matching/reinforcement-learning/research

papersTODAY 04:00 UTC

AbFlow: Paratope-Centric Antibody Design via Interaction-Enhanced Flow Matching

A preprint introduces AbFlow, a generative approach that models full-atom antibody structures end-to-end while focusing on the paratope, the region that contacts the antigen. The method uses flow matching augmented with interaction information to guide design. The authors position it as addressing gaps in existing antibody design pipelines, which have lacked a unified generative framework at this structural resolution.

protein-design/flow-matching/generative-models/research

papersTODAY 04:00 UTC

CyFM: Cylindrical Optimal Transport Method for Few-Step Complex-Valued Flow Matching

A new arXiv paper proposes CyFM, a technique that applies optimal transport on a cylindrical geometry to generate complex-valued signals in few sampling steps. Rather than treating data such as MRI scans and audio spectrograms as flat two-channel Euclidean inputs, the method models amplitude and phase separately. The authors argue this representation better matches the structure of complex signals for generative modelling.

flow-matching/optimal-transport/mri/arxiv

papersTODAY 04:00 UTC

Sparse Matrix-Decomposition Init Method Targets Flow-Matching Fine-Tuning Costs

A new arXiv preprint proposes a restricted initialization scheme for flow-matching diffusion models based on sparse matrix decomposition, aimed at reducing the cost of adapting these models to downstream tasks. The work builds on the observation that fine-tuning flow-matching models is expensive, and that low-rank adaptation combined with timestep-aware choices may help. The abstract suggests the method constrains initialization at a principal timestep to improve training efficiency.

flow-matching/diffusion-models/fine-tuning/lora

papersSEP 12 04:00 UTC

Model-Aware Diffusion Schedules Derived via Optimal Transport

A new arXiv paper argues that the schedules controlling how signal and noise are mixed along diffusion and flow-matching paths can be optimized by minimizing a kinetic action borrowed from optimal transport theory. The authors show that making these schedules depend on the specific model, rather than using fixed hand-tuned coefficients, improves generation quality. The work offers a theoretical framing for why certain noise schedules perform better than others.

diffusion/optimal-transport/generative-models/arxiv

papersSEP 11 04:00 UTC

Paper links continuous and discrete flow matching through argmax projection

A new arXiv preprint argues that continuous and discrete flow matching, typically developed as separate frameworks, are dual formulations of the same underlying construction. The authors show that applying a position-wise argmax to continuous convex-interpolant flows with one-hot targets recovers the discrete counterpart, and they examine how the choice of source distribution shapes categorical generation. A revised version of the submission has replaced the initial preprint.

flow-matching/generative-models/discrete-diffusion/research

papersSEP 11 04:00 UTC

Prediction-Loss Alignment for Sampler-Robust Flow Matching Training

The paper looks at a widely used training recipe for diffusion and flow-matching models, where the network predicts a clean sample that is then converted into a velocity for the loss. That conversion amplifies errors near the endpoints of the noise schedule, making training unstable and tied to a particular sampler. The authors propose aligning the prediction objective with the loss objective so that training stays robust regardless of which sampler is used at inference.

flow-matching/diffusion/training/arxiv