GIFT: LLM-Guided State-Reward Interface for Financial Reinforcement Learning
Financial portfolio trading is naturally formulated as a reinforcement learning problem, where an agent sequentially rebalances assets under changing market conditions to balance return, risk, and transaction costs. Yet in non-stationary markets, raw OHLCV states and short-horizon return rewards often provide an under-specified learning interface, motivating large language models as a way to inject financial knowledge into state and reward design while constraining open-ended generation. To this end, we propose GIFT, an LLM-guided framework for state-reward interface design in PPO-based financial reinforcement learning. Rather than using the LLM to make trading decisions, GIFT uses Factor...
| Authors | Yanyan Wu, Boyi Zhang, Yanlin Liu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 7 June 2026 |
| Last updated at source | 7 June 2026 |
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