Agent World Model: Scaling Agentic RL with Synthetic Verifiable Environments

Authors: Agent World Model Team (2026)

arXiv: 2602.10090

Domains

ApplicationsReasoning

TLDR (English)

Trains tool-use agents at scale in procedurally generated, verifiable synthetic environments (covering MCP tool calls), running over a thousand environment instances in parallel per step. It sidesteps the cost, latency, and irreproducibility of real environments and is a representative 2026 work on environment synthesis for agentic RL.

TLDR（中文）

用程序生成、可验证的合成环境大规模训练 Agent 的工具使用能力（覆盖 MCP 工具调用），单步可并行上千个环境实例，绕开真实环境昂贵、缓慢且不可复现的瓶颈，是 2026 年 Agentic RL 环境合成的代表工作。

Appears in These Articles

• Agent 与工具使用：模型不只是聊天
• 微调与对齐：让模型听指令、守规矩
• Agents and Tool Use: Models Are More Than Chat
• Fine-Tuning and Alignment: Making Models Follow Instructions

Co-cited Papers

These papers appear in the same articles as this one

• Model Context Protocol (MCP) Anthropic (2024)
• Constitutional AI: Harmlessness from AI Feedback Yuntao Bai et al. (2022)
• Deep Reinforcement Learning from Human Preferences Paul Christiano et al. (2017)
• The Evaluation Differential: How Evaluation Awareness Distorts LLM Benchmarks The Evaluation Differential Team (2026)

Related Papers

Other papers in the same domain

• Adaptive Parallel Reasoning (UC Berkeley Technical Report EECS-2026-213) UC Berkeley (2026)
  ReasoningInference
• Kimi K3: Open-Weight Trillion-Scale MoE for Agentic Intelligence Moonshot AI (2026)
  Mixture of ExpertsMultimodalApplications
• On-Policy Self-Distillation for Reasoning Models Self-Distilled Reasoner Team (2026)
  AlignmentReasoning
• DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning DeepSeek-AI (2025)
  ReasoningAlignment
• Model Context Protocol (MCP) Anthropic (2024)
  Applications
• SWE-bench: Can Language Models Resolve Real-World GitHub Issues? Carlos E. Jimenez et al. (2024)
  EvaluationApplications