AI RESEARCH
FPMoE: A Sparse Mixture-of-Experts Approach to Functional Code Generation
arXiv CS.AI
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ArXi:2605.27849v1 Announce Type: cross Despite rapid progress in LLM-based code generation, existing models are predominantly trained on imperative languages, leaving functional programming languages (FPLs) such as Haskell, OCaml, and Scala chronically underexplored, with even frontier models performing substantially worse on FPLs. Fine-tuning is a natural remedy, but our experiments show that per-language fine-tuning fails to capture shared functional abstractions, while merged multi-language fine-tuning.