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.
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.
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.
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.
