# Horizon Produto de ambientes RL da Labelbox para pós-treinamento e avaliação de tarefas de IA de longo horizonte. - Findings24: - Site: - Preços: Freemium - Categorias: Avaliação de LLMs ## O que é Horizon? Horizon is Labelbox’s RL environment product for post-training and evaluation. It focuses on generating software-based environments, preference signals, and rubrics for long-horizon AI tasks where reward signal quality matters. The product is positioned for reasoning, tool use, and computer use across economically important knowledge-work domains. The site describes use cases such as autonomous AI research, scientific knowledge work, agent coding, multimodal work, voice with agentic tool use, computer use, and cybersecurity. ## O que Horizon pode fazer? ### Post-training environments Build RL environments and preference-signal datasets for post-training and evaluation, with an emphasis on calibrated reward signals and pass@k targets. ### Reasoning and agent-task support Model tasks around reasoning, tool use, and computer use, including long-horizon workflows where verification and intermediate rewards matter. ### Enterprise workflow simulation Use WorldSim-style simulations to recreate enterprise environments such as GitLab, Jira, CRM, email, and chat with realistic business data. ### Scenario generation at scale Design configurable scenarios that generate diverse tasks at scale, including outages, workflow changes, and other long-tail situations. ### Preference-signal labeling Capture preference labels across agent trajectories to turn human judgment into structured comparisons for reward modeling. ### Cross-domain coverage Apply the same environment and evaluation approach across knowledge work domains such as autonomous research, software engineering, multimodal work, voice agents, computer use, and cybersecurity. ## Casos de uso ### Enterprise workflow simulation Create simulations for enterprise knowledge work so agents can practice tasks in systems that resemble GitLab, Jira, CRM, email, and chat. ### Reward modeling for long tasks Generate reward signals and rubrics for long-horizon tasks where models need intermediate verification rather than only final-answer scoring. ### Agent coding and software engineering Build training and eval sets for software agents that debug, author PRs, and work through multi-step software engineering workflows. ### Cybersecurity evaluation Construct scenarios for cybersecurity work, including attack and defense tasks with programmatic verification and adversarial edge cases. ### Multimodal knowledge work Tune environments for knowledge work that spans text, images, charts, documents, and structured data within a single workflow. ## Perguntas frequentes ### What is Horizon used for? Labelbox positions Horizon as RL environments and preference-signal infrastructure for post-training and evaluation. It is built for reasoning, tool use, computer use, autonomous research, scientific knowledge work, agent coding, and cybersecurity. ### How does Horizon support training and evaluation? The source describes Horizon as software-generated RL environments at scale, with calibrated reward signals and preference labels. It emphasizes scenarios, verification, difficulty progression, and credit assignment for long-horizon tasks. ### Who is Horizon for? Horizon is presented for frontier AI labs and teams working on long-horizon, economically important knowledge-work domains. The examples shown include scientific knowledge work, agent coding, computer use, and cybersecurity. ### Does Horizon have public pricing? The source does not list public pricing or packaging details for Horizon. It provides a contact path and a start-for-free call to action on the main site. ## Informações rápidas - **Category**: RL environments - **Platform**: Labelbox - **Primary use**: Post-training and evaluation - **Site**: labelbox.com - **Related workflows**: Reasoning, tool use, computer use