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This paper introduces the Agentic Learning Ecosystem (ALE), an open-source infrastructure comprising ROLL (a post-training framework), ROCK (a sandbox environment manager), and iFlow CLI (an agent framework), designed to streamline agentic model development. They release ROME, an agent trained within ALE on over a million trajectories, utilizing data composition protocols for complex behavior synthesis and a novel Interaction-Perceptive Agentic Policy Optimization (IPA) algorithm for improved long-horizon training. Empirical evaluations on benchmarks like SWE-bench Verified and Terminal Bench Pro demonstrate ROME's strong performance, validating the effectiveness of the ALE ecosystem.
An open-source ecosystem for agentic learning, complete with a trained agent and novel policy optimization, promises to accelerate research by providing a standardized, scalable platform.
Agentic crafting requires LLMs to operate in real-world environments over multiple turns by taking actions, observing outcomes, and iteratively refining artifacts. Despite its importance, the open-source community lacks a principled, end-to-end ecosystem to streamline agent development. We introduce the Agentic Learning Ecosystem (ALE), a foundational infrastructure that optimizes the production pipeline for agentic model. ALE consists of three components: ROLL, a post-training framework for weight optimization; ROCK, a sandbox environment manager for trajectory generation; and iFlow CLI, an agent framework for efficient context engineering. We release ROME, an open-source agent grounded by ALE and trained on over one million trajectories. Our approach includes data composition protocols for synthesizing complex behaviors and a novel policy optimization algorithm, Interaction-Perceptive Agentic Policy Optimization (IPA), which assigns credit over semantic interaction chunks rather than individual tokens to improve long-horizon training stability. Empirically, we evaluate ROME within a structured setting and introduce Terminal Bench Pro, a benchmark with improved scale and contamination control. ROME demonstrates strong performance across benchmarks like SWE-bench Verified and Terminal Bench, proving the effectiveness of ALE.
