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The tweet discusses the growing use of continuous flow models for discrete tasks like language generation, proposing a method to represent vocabulary as one-hot vectors and learn flows in continuous space.
This paper introduces Newton Matching, a unified framework for fine-tuning and sampling in generative models, which addresses limitations of existing methods by treating learning as an iterative optimization process and leveraging conditional-matching structure.
PathGuide reformulates classifier-free guidance selection as an on-policy transport problem in flow-based generative models, using the weak form of the continuity equation to dynamically optimize guidance scales for improved sample fidelity.
The article introduces ActFlow, a continued pre-training scheme that expands the valid design space for flow and diffusion models, enabling out-of-distribution generative modeling and evolvable search spaces in scientific discovery.
This paper proposes MeanFlow-Transfer (MF-T) and Continuous Adversarial MeanFlow (CAMF) to unify the adaptation and acceleration of pretrained diffusion and flow models, enabling high-quality few-step generation on new domains with limited data.
GS-Voxel introduces a fitting-free framework to convert 3D Gaussian Splatting reconstructions into structured latents, enabling scalable generation of large-scale aerial 3D scenes via flow models and tiled inference.
Introduces CrystalGRPO, a reinforcement-learning post-training framework for flow-based crystal structure prediction that aligns target recovery and preserves candidate coverage, improving Top-1 and Top-20 performance across MP-20 and MPTS-52 benchmarks.
This paper introduces LC-GRPO, a flow-based GRPO framework with Langevin correction that bridges the train-inference gap by aligning stochastic training rollouts with deterministic ODE sampling, improving reward optimization on models like SD3.5, FLUX.1-Dev, and HunyuanVideo.
FLUX 3 proposes multimodal flow models as a foundational approach for real-world visual intelligence, building on prior FLUX work.
MeanFlowNFT introduces a forward-process reinforcement learning method for average-velocity generators, enabling efficient alignment with human preferences while preserving fast few-step sampling. Experiments show it outperforms prior RL-tuned few-step generators on most metrics and even surpasses multi-step RL-tuned diffusion models.
Flow Reasoning Models (FRMs) introduce a training and test-time-scaling framework for discrete flow models on structured reasoning tasks. By using self-verification and self-conditioning, FRMs achieve nearly 100% solve rates on Sudoku and Zebra puzzles with far fewer passes than previous baselines.
This paper introduces Masked Language Flow Models (MLFMs), which incorporate masking into flow-based language models to enable continuous flow for conditional generation and allow pretrained Masked Diffusion Models to be converted. The authors propose a novel sampler that alternates continuous denoising with discrete unmasking, demonstrating for the first time that flow-based language models can scale to downstream reasoning and instruction-following tasks.
FlowBender is a closed-loop framework that improves constraint satisfaction in diffusion and flow models by training networks to correct alignment errors using inference-time feedback, outperforming traditional supervised and guidance-based approaches.
Proposes treating flow steps as RL actions combined with a 'flow reversal' technique for flow offline reinforcement learning.
QGF is an RL algorithm that improves policies at test time by using a value gradient to guide a pre-trained flow policy, avoiding training-time instability while maintaining competitive performance.
This paper explains the root cause of reward hacking in reward-guided flow and diffusion models, attributing it to finite-particle plug-in estimation of the Doob h-function, and proposes a reward damping schedule to correct within-mode bias without additional computational cost.
Introduces Constrained Flow Optimization (CFO), a framework for fine-tuning generative flow models to maximize rewards while satisfying constraints in molecular design, with theoretical guarantees and experimental validation.
The paper identifies off-manifold drift in guided flow models under compositional rewards and proposes Conflict-Aware Additive Guidance (CAR), a lightweight method that dynamically resolves gradient conflicts to improve generation fidelity without retraining.
Flow-Direct introduces a non-parametric guidance field for flow-based generative models that accumulates reward feedback persistently, improving feedback efficiency and enabling reuse of collected samples to guide generation for multiple objectives without additional reward evaluations.