AI模型精选73°

Terminal-Universe:将代理轨迹转化为可扩展终端环境

Terminal-Universe: Turning Agent Trajectories into Scalable Terminal Environments

精选理由

Terminal-Universe能把代码执行轨迹变成可复用环境,让Qwen3.5-27B在终端任务上提升近12分。

AI 摘要

Terminal-Universe框架将终端代理轨迹转化为可重用环境,从轨迹中恢复文件操作并重建工作空间。该框架应用于公共终端代理轨迹后,生成了37.3k个任务充分的环境。使用该语料库对Qwen3.5-27B进行监督微调,使其在Terminal-Bench 2.1上的单轮性能提升11.9分,在EvoCode-Bench v2 MT@4上的多轮性能提升13.8分。

原文 · arXiv cs.AI

Terminal-Universe: Turning Agent Trajectories into Scalable Terminal Environments

As terminal-based code agents become prevalent, agent trajectories have accumulated at scale, while realistic, executable environments remain scarce. However, environments are what agent post-training actually requires: each can be re-queried into many verifiable tasks and provides execution feedback, whereas a trajectory is a single frozen demonstration. Rather than generating environments from scratch, we observe that the tool-execution history in existing trajectories exposes the structure and contents of the environments in which they ran, making it possible to reconstruct those environments from the trajectories themselves. Thus, we introduce Terminal-Universe, a framework which turns each trajectory into a reusable environment and explores it for synthesizing new tasks and continued interactions. Specifically, Terminal-Universe replays the file operations recorded in a trajectory to restore each file before the agent modified it, yielding a partial workspace; a completion agent then supplies the missing files and dependencies. On this recovered workspace, we both reconstruct the original intent task and synthesize entirely new ones. Besides, we also scale the tasks along two complementary axes: breadth and depth. For breadth, we mine directional dependency relations between related environments and synthesize cross-workspace queries spanning multiple codebases, as developers routinely do in real-world development. For depth, we extend the initial single-turn query into a multi-round session that captures iterative user feedback and requirement refinement via a user agent. Applied to public terminal agent trajectories, Terminal-Universe produces 37.3k task-sufficient environments. Supervised fine-tuning of Qwen3.5-27B on this corpus improves single-round performance on Terminal-Bench 2.1 by 11.9 points and multi-round performance on EvoCode-Bench v2 MT@4 by 13.8 points.