@inproceedings{farazi-reza-2025-troopers,
    title = "Troopers at {BLP}-2025 Task 2: Reward-Selective Fine-Tuning based Code Generation Approach for {B}angla Prompts",
    author = "Farazi, Musa Tur  and
      Reza, Nufayer Jahan",
    editor = "Alam, Firoj  and
      Kar, Sudipta  and
      Chowdhury, Shammur Absar  and
      Hassan, Naeemul  and
      Prince, Enamul Hoque  and
      Tasnim, Mohiuddin  and
      Rony, Md Rashad Al Hasan  and
      Rahman, Md Tahmid Rahman",
    booktitle = "Proceedings of the Second Workshop on Bangla Language Processing (BLP-2025)",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.banglalp-1.54/",
    pages = "561--565",
    ISBN = "979-8-89176-314-2",
    abstract = "We present a formally grounded description of a reward-selective fine-tuning (RSFT) pipeline for code generation from Bangla natural-language prompts. The implemented system mines candidate programs via temperature and nucleus sampling, executes candidates in a sandbox and retains programs that pass all unit tests, performs supervised fine-tuning (SFT) on winners using parameter-efficient Low rank adaptation (LoRA) adapters, and augments robustness through fuzzed asserts. We specify the exact objectives and estimators used, provide a Bangla-aware preprocessing recipe, prove simple properties of the sampling budget, and report an ablation showing the effect of inference sample budget $K$ on accuracy. We also include a threat model for safe execution. Our codes are available on GitHub."
}