GIFT uses LLMs for factor-guided state enhancement, risk-rule reward shaping, and diagnostic refinement in PPO financial RL, then fixes the interface to improve out-of-sample risk-adjusted performance.
Proceedings of the AAAI Conference on Artificial Intelligence , author=
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FPILOT optimizes pre-trained RL trading policies at inference time using forecasted price trajectories to improve portfolio allocations and risk-adjusted returns on the DJ30 benchmark.
citing papers explorer
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GIFT: LLM-Guided State-Reward Interface for Financial Reinforcement Learning
GIFT uses LLMs for factor-guided state enhancement, risk-rule reward shaping, and diagnostic refinement in PPO financial RL, then fixes the interface to improve out-of-sample risk-adjusted performance.
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Plan Before You Trade: Inference-Time Optimization for RL Trading Agents
FPILOT optimizes pre-trained RL trading policies at inference time using forecasted price trajectories to improve portfolio allocations and risk-adjusted returns on the DJ30 benchmark.