ARLArena: A Unified Framework for Stable Agentic Reinforcement Learning

A research team led by UCLA's Xiaoxuan Wang and 13 collaborators has introduced ARLArena, a comprehensive framework addressing the critical instability problem in agentic reinforcement learning (ARL). While ARL shows promise for training AI agents on complex, multi-step interactive tasks, current methods suffer from frequent training collapse that limits scalability to larger environments and prevents systematic exploration of algorithmic choices. The researchers first constructed a clean, standardized testbed to examine training stability in controlled settings, then decomposed policy gradients into four core design dimensions to assess performance and stability systematically.

Through this fine-grained analysis, the team distilled a unified perspective on ARL and developed SAMPO (Stable Agentic Policy Optimization), a method specifically designed to mitigate the dominant sources of instability in agent training. Empirically, SAMPO demonstrates consistently stable training and strong performance across diverse agentic tasks, representing a significant advancement toward reliable AI agent development. This research provides both theoretical insights and practical guidance for building stable, reproducible training pipelines for LLM-based agents, potentially accelerating progress toward more capable autonomous systems that can handle longer interaction horizons and more complex environments.