Plan Before You Trade: Inference-Time Optimization for RL Trading Agents
This paper introduces FPILOT, an MPC-inspired inference-time optimization framework that enables RL trading agents to adapt their policies using price forecasts at trade time without retraining. Evaluated on the TradeMaster DJ30 benchmark, FPILOT consistently improves total return and risk-adjusted metrics across five algorithms, with gains increasing as forecaster quality improves.
[2605.12653] Plan Before You Trade: Inference-Time Optimization for RL Trading Agents
[Submitted on 12 May 2026]
Title:Plan Before You Trade: Inference-Time Optimization for RL Trading Agents
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Abstract:Reinforcement learning agents for portfolio management are typically trained and deployed as static policies, with no mechanism for using price forecasts at inference time. We propose $\text{FPILOT}$ (Financial Plugin Inference-time Learning for Optimal Trading), a plugin inference-time optimization framework inspired by Model Predictive Control (MPC). Our key structural insight is that future prices mostly do not depend on one agent's portfolio allocation, so a suitable predictive model can produce a multi-step price trajectory without iterative action-conditioned rollouts as in typical reinforcement learning. At each decision step, we use the forecaster's predicted price trajectory to construct an allocation-based imagined return objective, and optimize the policy at inference-time before executing one step of the trade. Our framework is compatible with any pre-trained agent and adapts the policy to the forecaster's predictions without any retraining. Evaluated across five policy learning algorithms on the TradeMaster DJ30 benchmark, $\text{FPILOT}$ produces consistent improvements in total return and return-based risk-adjusted metrics (Sharpe, Sortino, Calmar), with stochastic policies benefiting more than deterministic ones. Further, using synthetic forecasts at calibrated quality levels, we show that gains consistently improve with forecaster quality, suggesting that our performance will improve based on advances in financial forecasting.
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
Cite as: arXiv:2605.12653 [cs.LG]
(or arXiv:2605.12653v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2605.12653
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Rohan Deb [view email] [v1] Tue, 12 May 2026 18:58:03 UTC (199 KB)
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