AlphaRLM: Aligning Language Models for Literature-Grounded Symbolic Factor Discovery

Abstract:Formulaic alpha mining seeks to discover executable expressions that capture predictive market signals in a vast, noisy search space. Recent LLM-based approaches use prompting, iterative search, or seed-factor evolution, leaving direct factor construction through policy learning less explored. To address this, we introduce AlphaRLM, a reinforcement-learning framework that trains LLMs to construct executable symbolic factors from financial literature without input factor expressions. AlphaRLM incorporates Literature-Guided Operator Assembly (LEGO), which conditions factor construction on literature-derived ideas and executable operators, providing semantic priors for exploration. It further aligns the generation policy with market feedback through executable-validity and grouped-return-separation rewards that capture non-monotonic structures across factor quantiles. Extensive experiments on CSI 300, CSI 1000, and S&P 500 show that AlphaRLM outperforms strong factor-mining and LLM-based baselines in annualized excess return. Notably, the 8B model achieves strong cross-market performance, highlighting the promise of literature-conditioned, market-aligned factor generation.

Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG); Portfolio Management (q-fin.PM)

arXiv:2610.14959 [cs.AI]

(or arXiv:2610.14959v3 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.14959 Focus to learn more arXiv-issued DOI via DataCite

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