arXiv:2511.14460v2 Announce Type: replace Abstract: Large language models (LLMs) have rapidly evolved from single-turn text generators into the foundation of increasingly capable agents. As these agents take on more complex reasoning, decision making, tool use, and long-horizon tasks, reinforcement learning (RL) is becoming increasingly important for shaping their behavior. This shift is especially visible in agentic RL, where models must interact with tools and environments across multiple rounds rather than produce a single standalone response. In this regime, the usual view of a trajectory
This paper addresses the rapidly evolving capabilities of large language models and the increasing need for sophisticated agentic behaviors, which is a focal point of current AI research and development.
The development of unified and modular frameworks for agentic reinforcement learning is critical for advancing autonomous AI systems beyond narrow applications to more complex, real-world tasks.
This framework could accelerate the development and deployment of more robust and adaptable AI agents, making their creation and iteration more efficient.
- · AI software developers
- · Companies adopting AI agents
- · Cloud computing providers
- · Research institutions
- · Businesses reliant on manual white-collar workflows
- · Legacy software providers
Improved efficiency and autonomy of AI agents across various industries.
Increased demand for specialized AI training data and computational resources.
Significant restructuring of the workforce as AI agents automate complex tasks, leading to new job categories and economic models.
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