RyanShen1010/influence-curriculum-lean4

The project represents a niche research implementation at the intersection of 'Data-Centric AI' and formal verification. While applying influence functions to theorem proving curriculum is a sophisticated academic approach, the project currently lacks any defensive moat. With 0 stars and being only 1 day old, it functions as a personal research artifact rather than a tool with ecosystem gravity. Automated Theorem Proving (ATP) in Lean 4 is a high-priority frontier for labs like Google DeepMind (AlphaProof) and OpenAI; these organizations are already deeply invested in synthetic data generation and curriculum optimization for reasoning tasks. The methodology used here—quantifying training example contributions—is exactly the type of optimization frontier labs integrate into their proprietary training pipelines once validated. Key competitors include established frameworks like LeanDojo and internlm-math, which already possess significant community momentum. There is no evidence of a unique dataset or specialized infrastructure that would prevent a larger lab or a more established open-source project from absorbing this methodology within months.