Think-Then-Trade: A Dual-Speed Reinforcement Learning Framework for Risk-Aware Financial Decision Making with Large Language Models
Keywords:
reinforcement learning, large language models, financial decision making, risk management, dual-speed reasoning, autonomous trading, cognitive architecture, system governanceAbstract
The intersection of reinforcement learning and large language models presents a transformative opportunity for financial decision making, yet existing approaches often fail to balance the competing demands of rapid market responsiveness and deliberate risk evaluation. This paper introduces Think-Then-Trade, a dual-speed reinforcement learning framework that integrates a fast, reflexive trading policy with a slow, deliberative planning module, both orchestrated through a shared large language model interface. Drawing on cognitive science principles of dual-process reasoning, the framework separates low-latency execution from high-level strategic reasoning. The fast module uses a lightweight actor-critic architecture trained online to capture transient market signals, while the slow module leverages a transformer-based language model to simulate future scenarios, incorporate textual news and sentiment, and generate explicit risk constraints. The two speeds communicate via a meta-controller that dynamically arbitrates control based on market volatility, uncertainty estimates, and model confidence. We evaluate the framework on synthetic and historical market data, demonstrating superior risk-adjusted returns compared to single-speed baselines and improved robustness during regime shifts. Beyond performance, the paper discusses system-level implications including governance of autonomous trading agents, fairness and transparency in model deployment, computational sustainability of dual-model architectures, and policy considerations for regulatory oversight. The Think-Then-Trade framework offers a scalable paradigm for embedding human-like deliberation into automated financial systems, with broad applicability to domains requiring simultaneous speed and prudence.
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