arXiv:2604.18401v2 Announce Type: replace Abstract: Agentic reinforcement learning (RL) is emerging as a critical post-training paradigm for improving LLM agent capabilities. Existing RL algorithms for LLMs largely follow the token-centric paradigm as in RLHF and RLVR, where tokens serve as the basic units for modeling and optimization. However, this paradigm introduces a granularity mismatch in agentic RL, as it optimizes token-level predictions while LLM agents make step-level decisions through cycles of environmental observations and actions. To bridge this gap, we propose \textbf{StepPO},
This paper addresses a fundamental limitation in current LLM-based reinforcement learning, which is crucial as the field rapidly moves towards more autonomous agents.
Improving policies for agentic reinforcement learning directly enhances the capability and reliability of AI agents, accelerating their adoption and impact across industries.
Optimizing LLM agents at the step-level rather than token-level could lead to more robust, efficient, and intelligent agent behaviors, bridging a critical gap in agentic RL.
- · AI Agent Developers
- · Companies adopting AI Agents
- · LLM Research Community
- · AI-powered automation platforms
- · Companies with inefficient token-centric RL pipelines
More sophisticated and reliable AI agents become available for various tasks.
Increased efficiency and broader industrial deployment of AI agents lead to accelerated automation of complex workflows.
The enhanced capabilities of AI agents begin to displace certain white-collar tasks faster than previously anticipated, impacting labor markets.
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