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AlphaRJM: Reward-Jump Memory for Stochastic Return-Guided Alpha Discovery

arXiv · AI, language, vision and robotics · article · Sep 8, 2026 · UTC

Formulaic alpha discovery is a pool-dependent symbolic search problem in which informative feedback is observed primarily when a complete expression is evaluated. This delayed feedback creates two coupled difficulties: the retained alpha pool does not preserve the full history of realized evaluation feedback, and the value of an intermediate construction action is uncertain because its consequence depends on the formula eventually completed. We introduce AlphaRJM, which addresses these difficulties through Reward-Jump Memory, an event-driven latent state that remains fixed during token constru

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