arXiv:2602.21534v3 Announce Type: replace Abstract: Agentic reinforcement learning (ARL) has rapidly gained attention as a promising paradigm for training agents to solve complex, multi-step interactive tasks. Despite encouraging early results, ARL remains highly unstable, often leading to training collapse. This instability limits scalability to larger environments and longer interaction horizons, and constrains systematic exploration of algorithmic design choices. In this paper, we first propose ARLArena, a stable training recipe and systematic analysis framework that examines training stabi
The rapid development of AI necessitates breakthroughs in stable training methods for agentic systems, as current approaches face significant instability issues hindering real-world application.
Achieving stable agentic reinforcement learning is crucial for scaling AI agents to complex tasks, accelerating their practical adoption across various industries and collapsing white-collar workflows.
The introduction of a 'stable training recipe' could significantly reduce the current barriers to deploying sophisticated AI agents, shifting the focus from proof-of-concept to robust, scalable solutions.
- · AI development firms
- · Automation software providers
- · Researchers in reinforcement learning
- · Industries relying on complex decision-making
- · Tasks requiring human supervision due to AI instability
- · Inefficient AI agent development pipelines
- · Current manual workflow providers
More reliable and scalable AI agents become feasible for deployment in production environments.
Accelerated integration of autonomous AI systems into business operations, leading to significant efficiency gains and workforce restructuring.
Enhanced AI capabilities could drive new forms of economic activity and potentially exacerbate the compute and energy demands.
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