Agentic LLM Ecosystem for Real-World Tasks
Published:Dec 31, 2025 14:03
•1 min read
•ArXiv
Analysis
This paper addresses the critical need for a streamlined open-source ecosystem to facilitate the development of agentic LLMs. The authors introduce the Agentic Learning Ecosystem (ALE), comprising ROLL, ROCK, and iFlow CLI, to optimize the agent production pipeline. The release of ROME, an open-source agent trained on a large dataset and employing a novel policy optimization algorithm (IPA), is a significant contribution. The paper's focus on long-horizon training stability and the introduction of a new benchmark (Terminal Bench Pro) with improved scale and contamination control are also noteworthy. The work has the potential to accelerate research in agentic LLMs by providing a practical and accessible framework.
Key Takeaways
Reference
“ROME demonstrates strong performance across benchmarks like SWE-bench Verified and Terminal Bench, proving the effectiveness of the ALE infrastructure.”