This paper introduces Self-Improving GraphScout, a framework that refines training data for agentic graph reasoning through iterative generation, filtering, and model feedback. It includes a Quality-Aware Data Generation module, an Iterative Refinement Loop, and a Progressive Training Strategy, aiming for significant performance improvements over the baseline GraphScout method.
Key findings
Introduces a novel framework for iterative training data refinement in graph reasoning.
Includes a Quality-Aware Data Generation module for diverse and high-quality graph reasoning trajectories.
Implements an Iterative Refinement Loop using model performance signals to curate valuable training examples.
Adopts a Progressive Training Strategy to increase data complexity based on model capability estimates.
Limitations & open questions
The approach's effectiveness is yet to be validated through comprehensive experiments.
The scalability and computational efficiency of the framework need to be demonstrated.