RESEARCH · RESEARCH · #1004
AlphaDiverse: post-training local quantitative research agents for diverse alpha factor exploration (arXiv:2609.29014v1)
AlphaDiverse is a framework for LLM-based multi-agent alpha factor research that generates diverse research-path traces by varying plans and environments, then warm-starts local Planner and Realizer agents via supervised fine-tuning and jointly optimizes them with a GRPO method. The approach confines feedback to an inner period and evaluates a frozen final model on an outer period to avoid test-set tuning; experiments on four Chinese stock universes show competitive prediction combined with broader exploration.
KEY POINTS
- AlphaDiverse is a framework for LLM-based multi-agent alpha factor research that generates diverse research-path traces by varying plans and environments, then warm-starts local Planner and Realizer agents via supervised fine-tuning and jointly optimizes them with a GRPO method.
- The approach confines feedback to an inner period and evaluates a frozen final model on an outer period to avoid test-set tuning; experiments on four Chinese stock universes show competitive prediction combined with broader exploration.
- The method shows how locally post-trained agents and diversity-driven trace collection can reduce dependence on external APIs and research-path collapse while producing robust, out-of-sample-evaluated alpha discoveries.
WHY IT MATTERS
The method shows how locally post-trained agents and diversity-driven trace collection can reduce dependence on external APIs and research-path collapse while producing robust, out-of-sample-evaluated alpha discoveries.