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Huawei Cloud Developer Alliance
Huawei Cloud Developer Alliance
Apr 13, 2026 · Artificial Intelligence

How AReaL v1.0 Enables Scalable Agentic RL on Ascend NPU with AWEX Weight Sync

The new AReaL v1.0 release brings full Ascend NPU support, detailed installation guides, and a best‑practice example for training a 30B MoE model across four nodes, while the integrated AWEX weight‑sync mechanism dramatically reduces synchronization time, improving efficiency and stability for large‑scale Agentic RL workloads.

AWEXAgentic RLAscend NPU
0 likes · 12 min read
How AReaL v1.0 Enables Scalable Agentic RL on Ascend NPU with AWEX Weight Sync
AntTech
AntTech
Dec 4, 2025 · Artificial Intelligence

How AState Reduces Trillion‑Parameter RL Weight Sync to 6 Seconds

AState is a general‑purpose state data management system for reinforcement‑learning tasks that tackles low IO efficiency, slow weight synchronization, and state‑recovery challenges, achieving sub‑10‑second weight sync for trillion‑parameter models through a three‑layer architecture, zero‑redundancy transfers, and hardware‑aware co‑design, with the code openly available on GitHub.

AStateWeight Synchronizationhigh performance computing
0 likes · 23 min read
How AState Reduces Trillion‑Parameter RL Weight Sync to 6 Seconds
AntTech
AntTech
Nov 21, 2025 · Artificial Intelligence

How Awex Enables Sub‑Second TB‑Scale Weight Sync for Trillion‑Parameter RL Models

Awex is a high‑performance Python framework that synchronizes training and inference weights for trillion‑parameter reinforcement‑learning models in seconds, using unified conversion, metadata management, and NCCL/RDMA transfer plans, dramatically reducing RL training latency and supporting diverse parallel strategies.

Distributed TrainingPythonWeight Synchronization
0 likes · 17 min read
How Awex Enables Sub‑Second TB‑Scale Weight Sync for Trillion‑Parameter RL Models