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.
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Using common random numbers in rollout simulations provably reduces variance in relative utility estimates when a rollout policy is invoked beyond some depth.
citing papers explorer
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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.
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Using Common Random Numbers for Simulation-based Planning with Rollouts
Using common random numbers in rollout simulations provably reduces variance in relative utility estimates when a rollout policy is invoked beyond some depth.