Zhirong Qian | Searcharxiv

arXiv2026

psRL: Efficient Training for Agentic AI via Training-Time Prefix Sharing

In modern agentic AI training, the system bottleneck is shifting from rollout to update. Emerging sampling strategies such as tree-structured and step-wise RL greatly increase training sample volume while incurring relatively low marginal rollout cost, causing the update phase to dominate the end-to-end execution time. Crucially, this shift exposes a new optimization opportunity, as production traces reveal substantial prefix redundancy across training samples. In this paper, we propose psRL (prefix sharing for RL), a new training system for agentic AI designed to exploit prefix redundancy among training samples. Leveraging the global visibility and data immutability inherent to the update phase, psRL achieves efficient workload scheduling and memory management for distributed training. Specifically, psRL introduces two novel prefix-sharing mechanisms that enable flexible, fine-grained workload distribution across GPU workers, simultaneously optimizing prefix reuse and achieving load balancing. Moreover, psRL implements a new underlying KV cache manager that facilitates adaptable block-size allocation and dynamic KV caching, maximizing memory utilization while maintaining a high prefix hit rate. Evaluations using production traces demonstrate that psRL outperforms existing systems by up to 5.2x in throughput. The source code will be publicly available soon.

cs.DC↗

arXiv2024

Fed-Credit: Robust Federated Learning with Credibility Management

Aiming at privacy preservation, Federated Learning (FL) is an emerging machine learning approach enabling model training on decentralized devices or data sources. The learning mechanism of FL relies on aggregating parameter updates from individual clients. However, this process may pose a potential security risk due to the presence of malicious devices. Existing solutions are either costly due to the use of compute-intensive technology, or restrictive for reasons of strong assumptions such as the prior knowledge of the number of attackers and how they attack. Few methods consider both privacy constraints and uncertain attack scenarios. In this paper, we propose a robust FL approach based on the credibility management scheme, called Fed-Credit. Unlike previous studies, our approach does not require prior knowledge of the nodes and the data distribution. It maintains and employs a credibility set, which weighs the historical clients' contributions based on the similarity between the local models and global model, to adjust the global model update. The subtlety of Fed-Credit is that the time decay and attitudinal value factor are incorporated into the dynamic adjustment of the reputation weights and it boasts a computational complexity of O(n) (n is the number of the clients). We conducted extensive experiments on the MNIST and CIFAR-10 datasets under 5 types of attacks. The results exhibit superior accuracy and resilience against adversarial attacks, all while maintaining comparatively low computational complexity. Among these, on the Non-IID CIFAR-10 dataset, our algorithm exhibited performance enhancements of 19.5% and 14.5%, respectively, in comparison to the state-of-the-art algorithm when dealing with two types of data poisoning attacks.

cs.LG↗