flow-based

Latent-Foresight proposes an end-to-end framework that jointly learns a latent tokenizer and a flow-based generative dynamics model, explicitly structuring the latent space for temporal predictability and outperforming two-stage pipelines on future scene understanding tasks.

GenPO++ proposes a reversible generative policy optimization framework that uses history states as auxiliary memory in a high-order reversible ODE solver, enabling exact inversion and Jacobian-free likelihood-ratio computation for flow-based policies in reinforcement learning. It achieves competitive performance on large-scale control, fine-tuning, and real-world robotic tasks while improving stability and efficiency.

This paper introduces FLAS, a flow-based activation steering method that learns a concept-conditioned velocity field to steer language model activations at inference time. On the AxBench benchmark, FLAS is the first learned method to consistently outperform in-context prompting on held-out concepts without per-concept tuning.

OpenAI introduces Glow, an improved reversible generative model that simplifies the RealNVP architecture by replacing fixed permutations with learned 1x1 convolutions, enabling better information flow and significant performance improvements.