Scaling Agentic Data for Long-Horizon Terminal Intelligence
To address this, we introduce TerminalHorizon, a fully automated data synthesis engine for long-horizon terminal agents.
ProofPaper ↗
Key points
- Terminal agents are moving beyond local operations toward completing real-world professional tasks.
- Short trajectories can demonstrate commands and tool calls, but they do not fully show how agents accumulate evidence through exploration, use it to inform judgments, continually adjust strategies based on feedback, and turn intermediate results into final deliverables.
- High-quality long-horizon trajectories capture these agentic behavioral patterns and their connections within a single problem-solving process, providing richer experience for sustained autonomous work.
- Using this engine, we construct 1.5K task environments and perform supervised fine-tuning of Qwen3.5-27B on the resulting dataset to obtain TerminalHorizon-27B.
Sources (2)
- [1]Scaling Agentic Data for Long-Horizon Terminal IntelligencearXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 9, 07:55 AM
To address this, we introduce TerminalHorizon, a fully automated data synthesis engine for long-horizon terminal agents.
Terminal agents are moving beyond local operations toward completing real-world professional tasks.
- [2]Scaling Agentic Data for Long-Horizon Terminal IntelligenceHugging Face Daily Papers · Oct 9, 12:00 AM · same content
Extractive summary: sentences quoted from the sources.
Before this
- Oct 8, 2026Understanding Jev, the new model everyone is talking about
- Oct 8, 2026Beyond Imitation: A Framework and Benchmark for LLM-Assisted Peer Review
- Oct 8, 2026SuperNav: An Agentic Navigation System for Any Task in Any Scene
- Oct 8, 2026VibeEdit: Image Editing with Canvas Instructions
- Oct 8, 2026SpaceCast-Bench: Evaluating Predictive Spatial Reasoning in Vision-Language Models
- Oct 7, 2026Judging in Latent Space: Efficient Generative Reward Modeling via Semantics-Preserving Compression
