FinGym: Scaling Financial Agents via Grounded Environment Synthesis

Financial agents have the potential to automate complex workflows, such as market analysis, trading, and payment processing. Training such agents requires environments that are diverse, scalable, and financially grounded. However, real financial environments are costly and risky to access, while general environment synthesis may overlook financial states, rules, and operational constraints, producing tasks that require infeasible actions. We introduce FinGym, a framework that translates natural-language requirements into customized financial environments grounded in domain knowledge and financial constraints. FinGym adopts reusable financial entities, market data, and tools into environments with explicit roles, constraints, and workflows. The environment specifications guide both task generation and rubric synthesis, providing executable training tasks for agentic reinforcement learning. Using FinGym, we construct 73 financial environments spanning ten scenarios and synthesize 5,040 verified agentic tasks to train FinGym-4B and FinGym-9B. Across five financial-agent benchmarks, these agents achieve relative improvements of 5.8%–441.9% over their corresponding base models and competitive performance against strong proprietary models. These results highlight FinGym as a scalable and reliable generator of environments and tasks for training financial agents to meet diverse financial requirements. The code and data are available at https://anonymous.4open.science/r/anony-0D833.