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AReaL

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Sep 8, 2026 · Artificial Intelligence

AReaL v1.0.5 LoRA RL: Low-Rank Adaptation for Accessible Large Model RL on Ascend

This article details AReaL-Ascend v1.0.5's LoRA RL capabilities, explaining how low-rank adaptation reduces memory overhead for large model reinforcement learning, describing two Megatron LoRA weight update modes (adapter sync vs. merge), and covering cross-node LoRA RL, MoE support, XCCL communication, and Qwen3.6-27B examples for practical deployment.

AReaLAscend NPULoRA

0 likes · 6 min read

AReaL v1.0.5 LoRA RL: Low-Rank Adaptation for Accessible Large Model RL on Ascend

Huawei Cloud Developer Alliance

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

0 likes · 12 min read

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

Huawei Cloud Developer Alliance

Sep 5, 2026 · Artificial Intelligence

THD Packing & MTP Speculative Decoding Tame 64K Code Agent RL on Ascend NPUs

AReaL-Ascend v1.0.5 enables stable 64K-context Code Agent RL for Qwen3.6-27B on Ascend NPUs, using THD Sequence Packing to eliminate padding waste and MTP speculative decoding to accelerate rollout, achieving reward improvement from 0.56 to 0.94 over 120 steps with train-rollout consistency near 1.0.

AReaLAscend NPUCode Agent

0 likes · 12 min read

THD Packing & MTP Speculative Decoding Tame 64K Code Agent RL on Ascend NPUs

AntTech

Jul 3, 2026 · Artificial Intelligence

AReaL 2.0 Launch: Micro‑Service Architecture Brings Online RL to Agent Applications

AReaL 2.0 re‑architects agentic reinforcement learning as a set of decoupled micro‑services, allowing existing agents to join an online RL loop with minimal code changes while addressing engineering gaps such as data conversion, multi‑turn modeling, and weight synchronization.

AReaLKPop strategyRL micro-service

0 likes · 8 min read

AReaL 2.0 Launch: Micro‑Service Architecture Brings Online RL to Agent Applications

Machine Heart

Jul 2, 2026 · Artificial Intelligence

How AReaL 2.0 Accelerates Self‑Evolving Agents

AReaL 2.0 introduces an online reinforcement‑learning infrastructure that turns real‑world agent interactions into a learning loop, defining three pillars—trajectory data protocol, data proxy, and evolution control plane—to enable agents to not only execute tasks but continuously improve from their own experience.

AReaLAgentic RLLLM agents

0 likes · 16 min read

How AReaL 2.0 Accelerates Self‑Evolving Agents

AI Explorer

Mar 6, 2026 · Artificial Intelligence

AReaL: Lightning‑Fast Asynchronous RL Engine for Building High‑Performance LLM Agents

AReaL, an open‑source, fully asynchronous reinforcement‑learning platform co‑developed by Tsinghua University and Ant Group, dramatically speeds up training of complex LLM agents, offering a simple, stable, and hardware‑flexible solution for developers seeking industrial‑grade AI agents.

AI infrastructureAReaLAsynchronous Training

0 likes · 7 min read

AReaL: Lightning‑Fast Asynchronous RL Engine for Building High‑Performance LLM Agents

AntTech

Apr 21, 2025 · Artificial Intelligence

InclusionAI Community to Present AReaL Reinforcement Learning Framework and AWorld Multi‑Agent Framework at ICLR 2025

The InclusionAI open‑source community, initiated by Ant Group, will showcase the latest advances of its reinforcement‑learning framework AReaL and multi‑agent framework AWorld at the ICLR 2025 conference in Singapore, highlighting performance breakthroughs, open‑source contributions, and industry‑focused AI research.

AReaLAWorldAnt Group

0 likes · 5 min read

InclusionAI Community to Present AReaL Reinforcement Learning Framework and AWorld Multi‑Agent Framework at ICLR 2025