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AWEX

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Sep 6, 2026 · Artificial Intelligence

AReaL v1.0.5: Colocated Training/Inference & Multi-Teacher On-Policy Distillation for Efficient RL

AReaL-Ascend v1.0.5 introduces two major innovations: colocated training and inference on shared NPUs via Ray scheduling and AWEX IPC zero-copy, and Multi-Teacher On-Policy Distillation (MOPD) that fuses domain expert models into a single student using token-level teacher signals, demonstrated on Qwen3.6-27B with GRPO.

AReaLAWEXAscend NPU

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AReaL v1.0.5: Colocated Training/Inference & Multi-Teacher On-Policy Distillation for Efficient RL

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