The pursuit of energy-efficient and adaptive artificial intelligence (AI) has positioned neuromorphic computing as a promising alternative to conventional computing. However, achieving learning on these platforms requires techniques that prioritize local information while enabling effective credit assignment. Here, we propose noise-based reward-modulated learning (NRL), a novel synaptic plasticity rule that mathematically unifies reinforcement learning and gradient-based optimization with biologically-inspired local updates. NRL addresses the computational bottleneck of exact gradients by approximating them through stochastic neural activity, transforming the inherent noise of biological and neuromorphic substrates into a functional resource. Drawing inspiration from biological learning, our method uses reward prediction errors as its optimization target to generate increasingly advantageous behavior, and eligibility traces to facilitate retrospective credit assignment. Experimental validation on reinforcement tasks, featuring immediate and delayed rewards, shows that NRL achieves performance comparable to baselines optimized using backpropagation, although with slower convergence, while showing significantly superior performance and scalability in multi-layer networks compared to reward-modulated Hebbian learning (RMHL), the most prominent similar approach. While tested on simple architectures, the results highlight the potential of noise-driven, brain-inspired learning for low-power adaptive systems, particularly in computing substrates with locality constraints. NRL offers a theoretically grounded paradigm well-suited for the event-driven characteristics of next-generation neuromorphic AI.
HISR: Hindsight Information Modulated Segmental Process Rewards For Multi-turn Agentic Reinforcement Learning
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院航空航天信息研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Tencent Hunyuan（腾讯文脉）；School of Computer Science and Technology, Tianjin University（天津大学计算机科学与技术学院）
Noise-corrected GRPO: From Noisy Rewards to Unbiased Gradients
Department of Machine Learning, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE；Technology Innovation Institute, Abu Dhabi, UAE；Department of Robotics, Khalifa University, Abu Dhabi, UAE
Learning to Explore with Parameter-Space Noise: A Deep Dive into Parameter-Space Noise for Reinforcement Learning with Verifiable Rewards
Shanghai Innovation Institute, Shanghai, China（上海创新研究院）；College of Future Information Technology, Fudan University, Shanghai, China（未来信息科技学院，复旦大学）；Shanghai AI Laboratory, Shanghai, China（上海人工智能实验室）；The Chinese University of Hong Kong, Hong Kong, China（香港中文大学）
Noise Distribution Decomposition based Multi-Agent Distributional Reinforcement Learning
Zhejiang Lab（之江实验室）；College of Information Science and Electronic Engineering, Zhejiang University（浙江大学信息科学与电子工程学院）；Research Institute of China Telecom（中国电信研究院）
University of Toronto（多伦多大学）；Vector Institute（向量研究所）；University of California, Santa Cruz（加州大学圣克鲁兹分校）；University of Illinois, Urbana–Champaign（伊利诺伊大学厄巴纳-香槟分校）
Inference-Time Alignment of Diffusion Models with Direct Noise Optimization
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；DAMO Academy, Alibaba Group（阿里巴巴达摩院）；Hupan Lab, Zhejiang Province（浙江省湖畔实验室）；University of Minnesota, USA（美国明尼苏达大学）；Shenzhen Research Institute of Big Data（深圳大数据研究院）
Adapting Technical-Service LLM Agents with Latent Logic Augmentation, Robust Noise Reduction, and Hybrid Reward Modeling
School of Mathematics and Sciences, Fudan University, Shanghai, China（复旦大学数学科学学院，上海，中国）；Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China（复旦大学上海数学中心，上海，中国）；Alibaba Group, Hangzhou, China（阿里巴巴集团，杭州，中国）；Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China（脑启发式智能科学技术研究院，复旦大学，上海，中国）；Center for Applied Mathematics & Shanghai Key Laboratory of Contemporary Applied Mathematics, Fudan University, Shanghai, China（应用数学中心及上海当代应用数学重点实验室，复旦大学，上海，中国）
