arXiv:2608. 20011v1 Announce Type: new Abstract: Preference optimization is a standard alignment method for generative models, yet extending it to continuous-time dynamics remains non-trivial.
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arXiv:2609.09905v1 Announce Type: cross Abstract: Preference alignment for flow and diffusion models now spans online reinforcement learning and offline preference optimization, but the relation betw...
arXiv:2601. 22495v2 Announce Type: replace Abstract: Fine-tuning flow matching models is a central challenge in settings with limited data, evolving distributions, or computational constraints.
PMosFM introduces a preconditioned manifold matching framework that enables one‑step physics‑constrained generation by encoding constraints in a manifold decoder. The method learns transport in intrinsic coordinates, eliminating the need for residual losses or trajectory unrolling, and employs a geometric preconditioner and covariance transform to improve conditioning. Experiments demonstrate reduced training and sampling time compared to multi‑step baselines while maintaining comparable physical and distributional fidelity.
arXiv:2606. 11075v1 Announce Type: new Abstract: Aligning text-to-image flow matching models with human preferences via direct reward backpropagation is sample-efficient but hampered by two well-known pathologies: activations cannot be stored across the full sampling trajectory at modern model scale, and chained Jacobian products across steps inflate the reward gradient as it travels back to early indices.
Fenchel Tilt Flow Control (FTFC) is a new method for fine‑tuning pretrained generative models to arbitrary preference functions. It decouples utility optimization from model fitting by first learning reward and density‑ratio weights on pretrained samples, then freezing these weights to adjust a diffusion or flow model in a single importance‑weighted stage. The approach supports general f‑divergence penalties, achieves exact duality for concave utilities, and demonstrates up to 20× efficiency gains while outperforming baselines on image and molecule generation tasks.
arXiv:2604. 17415v3 Announce Type: replace-cross Abstract: Reward-based fine-tuning steers a pretrained diffusion or flow-based generative model toward higher-reward samples while remaining close to the pretrained model.