EvoMind: A Self-Evolving Alpha Factor Mining Agent via Process-Reward Reinforcement Learning

Alpha factor discovery aims to identify predictive, robust, and interpretable signals from a vast expression space. While large language models (LLMs) provide a promising alternative to conventional search-based methods, existing approaches mainly rely on one-shot generation or prompt-based refinement, limiting their ability to continuously improve from noisy market feedback. We propose EvoMind, a self-evolving alpha factor mining agent via process-reward reinforcement learning. EvoMind formulates factor discovery as an iterative agentic loop of hypothesis generation, factor implementation, tool-based evaluation, feedback reasoning, and factor revision. Rather than optimizing only final backtest performance, we introduce hierarchical process rewards that provide dense supervision throughout the mining trajectory, jointly encouraging executable, diverse, predictive, and out-of-sample stable factors. Furthermore, EvoMind learns from both successful and failed trajectories by diagnosing previous exploration outcomes and reusing the resulting experience to guide subsequent factor evolution. This enables the agent to progressively improve its factor-mining policy instead of repeatedly generating independent candidates. Experiments across multiple markets and out-of-sample settings demonstrate that EvoMind consistently discovers more robust, diverse, and sustainable alpha factors than strong search-based and LLM-based baselines. Our results highlight process-reward reinforcement learning and experience-driven self-evolution as an effective paradigm for autonomous alpha discovery.