← Back to all news
arXiv Machine Learning By Shengyu Feng, Tarun Suresh, Yiming Yang

Unsupervised Diffusion Solver for Combinatorial Optimization via Combinatorial Adjoint Matching

Read the original on arXiv Machine Learning →

arXiv:2605. 30920v2 Announce Type: replace Abstract: Diffusion-based neural solvers have shown strong promise for combinatorial optimization (CO), but existing methods typically rely on supervised training with large collections of near-optimal solutions.

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
Jul 17

A Continuous-Time Reinforcement Learning Framework for Fine-Tuning Discrete Diffusion Models

arXiv:2607. 14522v1 Announce Type: new Abstract: We formulate reinforcement learning (RL) in continuous time with discrete state spaces and possibly arbitrary action spaces via a stochastic control approach, where the state dynamics are modeled as a controlled continuous-time Markov chain (CTMC).

By Zikun Zhang, Jiayuan Sheng, David D. Yao, Wenpin Tang
llmsdiffusionreinforcement-learningfine-tuning
More like this →
arXiv Machine Learning
Aug 27

Bayesian Flow Networks for Offline Trajectory Planning

The paper introduces Bayesian Flow Networks for Offline Trajectory Planning (BFN-RL), a generative modeling framework that unifies discrete and continuous trajectory synthesis for offline reinforcement learning. Unlike prior diffusion models that rely on Gaussian noise, BFN-RL iteratively updates distribution parameters, enabling a categorical planner to produce future state sequences and an inverse-dynamics model to translate these states into actions. Experiments demonstrate that BFN-RL effectively generates trajectories in both discrete planning and continuous control tasks, highlighting its versatility across data modalities.

By Ludvig Killingberg, Helge Langseth
diffusionreinforcement-learning
More like this →