Title: AlphaAgentEvo: Evolution-Oriented Alpha Mining via Self-Evolving Agentic Reinforcement Learning
Primary area: foundation or frontier models, including LLMs
Keywords: Alpha Mining, Agentic AI, Quantitative Investment, Self-evolving Agent

Abstract: Alpha mining is central to quantitative investment; however, existing approaches struggle to facilitate the systematic evolution of alphas. Traditional methods, such as genetic programming, are unable to interpret human natural-language instructions and often fail to extract valuable insights from unsuccessful attempts, leading to low interpretability and inefficient exploration. Analogously, without mechanisms for systematic evolution, e.g., long-term planning and reflection, multi-agent approaches may easily fall into repetitive evolutionary routines, thereby failing to realize efficient self-evolution. To overcome these limitations, we introduce **AlphaAgentEvo**, a self-evolving Agentic Reinforcement Learning (ARL) framework for alpha mining, which moves alpha mining beyond the brittle “search–backtest–restart” cycle toward a continuous trajectory of evolution. Instructed by a hierarchical reward function, our agent engages in self-exploration of the search space, first learning basic requirements (e.g., valid tool calls) and then higher-level objectives (e.g., continuous performance improvements). Through this process, the agent autonomously acquires advanced behaviors such as long-horizon planning and reflective reasoning, establishing a systematic evolution mechanism and addressing the reasoning bottlenecks of large language models (LLMs) in this task. Extensive experiments demonstrate that AlphaAgentEvo achieves higher evolution success rates and generates more valid, diverse, and transferable alphas, consistently surpassing traditional methods and existing reasoning LLMs. Notably, with only 1.7B–4B parameters, it outperforms state-of-the-art reasoning models such as DeepSeek-R1 at the hundred-billion-parameter scale, highlighting the promise of ARL for next-generation alpha mining.

TL;DR: AlphaAgentEvo introduces a new evolution-oriented paradigm for alpha mining via self-evolving agentic reinforcement learning, outperforming traditional and LLM baselines—even surpassing state-of-the-art LLMs with only 1.7B–4B parameters.