arXiv AI
MCP-Universe RL: A Framework for Training MCP Tool-Use Agents via Reinforcement Learning
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Synthesize and Reward -- Reinforcement Learning for Multi-Step Tool Use in Live Environments
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Libra: Efficient Resource Management for Agentic RL Post-Training
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ToolVerse: Unlocking Massive Environments and Long-Horizon Tasks for Agentic Reinforcement Learning
arXiv:2607. 15660v1 Announce Type: new Abstract: While LLM agents demonstrate strong reasoning abilities in compact and well-defined scenarios, they struggle to maintain robustness and effectiveness when faced with large-scale, diverse, and dynamic real-world environments that demand seamless tool integration.
arXiv AI
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OpenTinker: Separating Concerns in Agentic Reinforcement Learning
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CodeMidas: Scaling Agentic Coding RL Environments from Code Itself
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