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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Reasoning in large output spaces proceeds via shortlisting then fine-grained reasoning; this characterization enables a mechanistic distillation strategy that outperforms standard distillation.
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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.