2026
论文总结-From Self-Evolving Synthetic Data to Verifiable-Reward RL:Post-Training Multi-turn Interactive Tool-Using Agents 论文总结-Agent World Model:Infinity Synthetic Environments for Agentic Reinforcement Learning 论文总结-EnvScaler:Scaling Tool-Interactive Environments for LLM Agent via Programmatic Synthesis 论文总结-Simulating Environments with Reasoning Models for Agent Training 论文总结-Close the Loop:Synthesizing Infinite Tool-Use Data via Multi-Agent Role-Playing 论文总结-Scaling Agent Learning via Experience Synthesis 论文总结-GenEnv:Difficulty-Aligned Co-Evolution Between LLM Agents and Environment Simulators