arXiv：cs.LG· Jie Huang, Yuecheng Hong, Yao Chen, Hao Zhong, Linfeng Xiao, Xudong Liu, Hao Xia, Jianxuan Xin·· 16 小时前
AlphaRLM：用强化学习让语言模型从文献中发现可执行符号因子
AlphaRLM: Aligning Language Models for Literature-Grounded Symbolic Factor Discovery
AI 导读
研究团队提出 AlphaRLM，一个强化学习框架，训练 LLM 直接从金融文献构建可执行符号因子，无需输入因子表达式。其 Literature-Guided Operator Assembly（LEGO）机制以文献思路和可执行算子为条件提供语义先验，并通过可执行有效性与分组收益分离奖励将生成策略与市场反馈对齐。
正文
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.
| Comments: | 25 pages, 5 figures |
| Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG); Portfolio Management (q-fin.PM) |
| Cite as: | arXiv:2610.14959 [cs.AI] |
| (or arXiv:2610.14959v2 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.14959 arXiv-issued DOI via DataCite |
Submission history
From: Jie Huang [view email]
[v1]
Sat, 10 Oct 2026 14:32:10 UTC (2,914 KB)
[v2]
Wed, 14 Oct 2026 12:33:55 UTC (2,914 KB)
来源：arXiv：cs.LG · arxiv.org