← Back to all news
arXiv AI By Yuheng Li, Di Yang, Haipeng Chen, Yanhai Xiong

HyCO: A Hybrid Neural Solver for Combinatorial Optimization

Read the original on arXiv AI →

HyCO is a hybrid neural solver that combines a sequential reinforcement learning (RL) solver with a global diffusion model (DM) to tackle combinatorial optimization problems. The RL component builds an initial solution prefix, after which HyCO switches to a conditional DM to finish the remaining decisions. The authors provide a theoretical framework showing that this hybrid approach yields lower expected regret than either method alone, identify an optimal trigger step for the switch, and implement a lightweight adaptive trigger based on policy entropy and RL‑DM disagreement, achieving consistent performance gains across benchmarks.

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 AI.

Related stories

arXiv Machine Learning
Sep 3

Recursive Value Learning for Long-Horizon Offline Goal-Conditioned RL

The paper introduces DCRL (Divide-and-Conquer RL), a method that recursively decomposes offline goal-conditioned reinforcement learning trajectories into a balanced binary tree. By training values from the leaves up to the root, DCRL avoids noisy max-based backups and reduces bootstrap depth from linear to logarithmic, thereby limiting error accumulation. Experiments on diverse goal-reaching tasks show that DCRL outperforms prior flat offline GCRL methods, achieving a higher average score on the most challenging long-horizon OGBench tasks.

By Hyeonseong Jeon, Youngwoon Lee
reinforcement-learning
More like this →
arXiv Machine Learning
Sep 3

Reinforcement learning to choose optimizers

The paper introduces a reinforcement learning framework that selects among a portfolio of gradient‑based and derivative‑free optimizers during a run. At each decision point a recurrent policy reads the current run state and chooses both the next optimizer and its usage duration, passing the best solution and step size forward. The method is trained with a decoupled actor‑critic using the same runtime distribution metric as evaluation, and on unseen problems it outperforms all individual portfolio optimizers except at the smallest budgets, remaining robust to distribution shift.

By Martin van der Schelling, Deepesh Toshniwal, Miguel A. Bessa
reinforcement-learning
More like this →
arXiv Machine Learning
Jul 20

Dichotomous Diffusion Policy Optimization

arXiv:2601. 00898v3 Announce Type: replace Abstract: Diffusion-based policies have gained growing popularity in solving a wide range of decision-making tasks due to their superior expressiveness and controllable generation during inference.

By Ruiming Liang, Yinan Zheng, Kexin Zheng, Tianyi Tan, Jianxiong Li, Liyuan Mao, Zhihao Wang, Guang Chen, Hangjun Ye, Jingjing Liu, Jinqiao Wang, Xianyuan Zhan
agentsdiffusionreinforcement-learningroboticsmultimodalbenchmarks
More like this →
arXiv Machine Learning
Oct 8

An Informational Curse of Horizon in Goal-Conditioned Policy Learning

The paper identifies an informational curse of horizon in goal-conditioned policy learning, showing that longer goal relabeling horizons during training can hurt policy generalization and performance. Controlled experiments with oracle planners separate training horizons from test horizons, revealing that behavioral cloning policies degrade with longer horizons while reinforcement learning mitigates this effect. The authors attribute this to reduced conditional mutual information between actions and hindsight-relabeled goals, and demonstrate that distilling short-horizon policy Jacobians into long-horizon policies improves performance, especially in combinatorial manipulation tasks.

By John L. Zhou, Yuxuan Dong, Jonathan C. Kao
reinforcement-learningroboticssafety
More like this →