arXiv:2609. 22833v1 Announce Type: new Abstract: We study personalized federated reinforcement learning, in which $n$ agents, each acting in its own Markov decision process, collaborate through a server to learn a shared MAML-style policy initialization that becomes effective for an individual agent once that agent adapts it with a single local policy-gradient step.
By Ali Beikmohammadi, Sarit Khirirat, Sindri Magn\'usson
arXiv:2607. 29398v1 Announce Type: new Abstract: Diffusion models have revolutionized generative tasks but incur high latency due to iterative denoising.
By Zhikang Xie, Xichen Ye, Yifan Wu, Haoshen Yu, Li chenan, Peizhu Gong, Weizhong Zhang, Cheng Jin
arXiv:2608. 06563v1 Announce Type: new Abstract: Machine learning and optimization have advanced together, with practical demands motivating new theory and theoretical breakthroughs enabling new applications.
By Grigory Malinovsky
arXiv:2607. 07693v1 Announce Type: cross Abstract: Reinforcement learning from human feedback (RLHF) has emerged as a powerful paradigm for aligning generative models with human preferences.
By Eric Zhu, Abhinav Shrivastava, Soumik Mukhopadhyay
arXiv:2608. 01556v1 Announce Type: new Abstract: Large language models are increasingly aligned to human preferences via reward modeling, but user preference data are sensitive and often cannot be centralized.
By Seongyoon Kim, Boryeong Cho, Jihwan Oh, Seokhyun Chung, Se-Young Yun
arXiv:2606. 31026v1 Announce Type: cross Abstract: We propose OTCache, a training-free framework for accelerating diffusion sampling via caching schedule prediction.
By Huanlin Gao, Fang Zhao, Qiang Hui, Fuyuan Shi, Shaoan Zhao, Yantao Li, Chao Tan, Ting Lu, Yuren You, Kai Wang, Shiguo Lian
arXiv:2606. 08854v1 Announce Type: cross Abstract: Standard Reinforcement Learning with Verifiable Rewards (RLVR) training allocates a fixed rollout budget to every query, without regard for what each query's difficulty means for the current policy.
By Shivchander Sudalairaj, Kai Xu, Akash Srivastava, Giorgio Giannone
arXiv:2608.29296v1 Announce Type: cross
Abstract: Larger batches reduce the variance of stochastic gradients per update and are therefore often expected to accelerate training. Yet whether this stati...
By Ziniu Li, Jinbo Wang, Guanhua Huang, Feiyuan Zhang, Pengbo Li, Alex Chen
arXiv:2606. 10124v1 Announce Type: cross Abstract: Federated learning (FL) is often subject to aggregation variance if clients do not consistently participate in training rounds.
By Haoran Zhang, Cain\~a Figueiredo Pereira, Marie Siew, Xutong Liu, Carlee Joe-Wong, Rachid El-Azouzi
arXiv:2608. 15660v1 Announce Type: cross Abstract: Federated learning (FL) enables privacy-preserving distributed model training but faces challenges from heterogeneous model architectures and limited communication resources at the network edge.
By Chenwang Liu, Yijun Liu, Chang Liu, Xu Zhang, Pengchao Han
Fed-GRPO is a federated training framework for Large Language Models that extends Group Relative Policy Optimization (GRPO) to operate without sharing raw data. It introduces three reward‑signal‑driven mechanisms—signal‑weighted aggregation, global reward calibration, and adaptive sparse communication—to guide client updates based on reward statistics. Experiments on mathematical reasoning tasks show that Fed‑GRPO outperforms other federated methods, approaches centralized performance, and reduces communication by up to 32× while allowing extreme compression with minimal accuracy loss.
By Pengxin Guo, Shuang Zeng, Zonggen Li, Weiying Zheng, Mengting Liu, Liangqiong Qu
arXiv:2511. 23310v3 Announce Type: replace-cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has emerged as an effective paradigm for post-training large language models, yet the design of its baselines and learning-rate schedules remains largely heuristic.
By Zixun Huang, Jiayi Sheng, Zeyu Zheng