AutoForge: Automated Environment Synthesis for Agentic RL
Research Paper#Reinforcement Learning, Agentic AI, Environment Synthesis🔬 Research|Analyzed: Jan 3, 2026 19:30•
Published: Dec 28, 2025 09:43
•1 min read
•ArXivAnalysis
This paper addresses the limitations of current reinforcement learning (RL) environments for language-based agents. It proposes a novel pipeline for automated environment synthesis, focusing on high-difficulty tasks and addressing the instability of simulated users. The work's significance lies in its potential to improve the scalability, efficiency, and stability of agentic RL, as validated by evaluations on multiple benchmarks and out-of-domain generalization.
Key Takeaways
- •Proposes AutoForge, a novel approach for automated environment synthesis in RL.
- •Addresses limitations of existing RL environments, particularly in terms of difficulty and user instability.
- •Introduces an environment-level RL algorithm to improve training efficiency and stability.
- •Evaluated on multiple agentic benchmarks, demonstrating effectiveness and out-of-domain generalization.
Reference / Citation
View Original"The paper proposes a unified pipeline for automated and scalable synthesis of simulated environments associated with high-difficulty but easily verifiable tasks; and an environment level RL algorithm that not only effectively mitigates user instability but also performs advantage estimation at the environment level, thereby improving training efficiency and stability."