arXiv:2606.09138v1 Announce Type: new Abstract: Agentic reinforcement learning (RL) has become an important post-training paradigm for turning LLMs from static chatbots into interactive agents, giving rise to representative applications such as OpenClaw. Existing work mainly focuses on policy optimization algorithms and training frameworks, but pays less attention to the full data lifecycle of agent-environment interactions, from data production to training consumption. To bridge this gap, we present Claw-R1, an interactive step-level data middleware system for agentic RL. Claw-R1 connects het
The rapid development of LLMs into interactive agents necessitates robust data infrastructure to manage complex agent-environment interactions effectively.
This development addresses a critical bottleneck in the scalability and reliability of agentic AI systems by providing structured data management, moving beyond simple policy optimization.
The focus expands from purely algorithmic advancements to include the end-to-end data lifecycle for AI agents, impacting how they are trained, deployed, and refined.
- · AI agents developers
- · Reinforcement learning researchers
- · Data infrastructure providers
- · LLM companies
- · Companies relying on ad-hoc RL data management
- · Less modular AI development approaches
Claw-R1 will enable more robust and scalable agentic AI applications by streamlining data handling.
Improved data management will accelerate the development and deployment of sophisticated AI agents across various industries.
The increased reliability of agentic AI could lead to broader societal integration of autonomous systems, impacting white-collar workflows significantly.
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Read at arXiv cs.LG