← Back to all news
arXiv Machine Learning By Jeongwoo Shin, Dongsoo Shin, Yuchen Zhu, Wei Guo, Yongxin Chen, Joonseok Lee, Jaewoong Choi, Jaemoo Choi

Efficient Adjoint Matching for Fine-tuning Diffusion Models

Read the original on arXiv Machine Learning →

Efficient Adjoint Matching (EAM) is a new method for reward fine‑tuning of pretrained diffusion models that reduces computational cost by reformulating the stochastic optimal control problem with a linear base drift and a modified terminal cost. This change eliminates the need for extensive stochastic simulation of full generative trajectories and backward ODE simulation of the adjoint state, allowing training‑time sampling with a few‑step deterministic ODE solver and a closed‑form adjoint solution. On standard text‑to‑image reward fine‑tuning benchmarks, EAM converges up to four times faster than the previous Adjoint Matching approach while matching or surpassing it on metrics such as PickScore, ImageReward, HPSv2.1, CLIPScore, and Aesthetics.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv Machine Learning
Jun 10

Exploring the Design Space of Reward Backpropagation for Flow Matching

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.

By Ruoyu Wang, Boye Niu, Xiangxin Zhou, Yushi Huang, Tongliang Liu, Chi Zhang
diffusion
More like this →
arXiv AI
Jul 1

Finite Difference Flow Optimization for RL Post-Training of Text-to-Image Models

arXiv:2603. 12893v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has become a standard technique for post-training diffusion-based image synthesis models, as it enables learning from reward signals to explicitly improve desirable aspects such as image quality and prompt alignment.

By David McAllister, Miika Aittala, Tero Karras, Janne Hellsten, Angjoo Kanazawa, Timo Aila, Samuli Laine
llmsdiffusionreinforcement-learningsafety
More like this →
arXiv Machine Learning
Jun 2

Drifting Preference Optimization for One-Step Generative Models

arXiv:2606. 02521v1 Announce Type: new Abstract: One-step text-to-image generators are attractive for deployment because they generate an image with a single forward pass, but preference finetuning them remains difficult: standard alignment methods often rely on policy likelihoods, denoising trajectories, differentiable reward gradients, or test-time optimization.

By Zhou Jiang, Yandong Wen, Zhen Liu
diffusionfine-tuningbenchmarkssafety
More like this →