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Quality over Quantity: Diversity-Aware Data Selection for Efficient Verilog Code Generation — arXiv:2609.22765

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.22765
License (as archived): CC0-1.0
Archived: 2026-10-09T06:55:55.391Z
SHA-256 of the archived text: d7031dc228ffa5c0c428052f6d1af80e0af76279db2b8ad4a60b344e9f4e0c25
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How to cite

Quality over Quantity: Diversity-Aware Data Selection for Efficient Verilog Code Generation — arXiv:2609.22765 — arXiv (en), https://arxiv.org/abs/2609.22765 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_39dfcf80b4ea (Metraipolis archive dc_39dfcf80b4ea, sha256 d7031dc228ffa5c0c428052f6d1af80e0af76279db2b8ad4a60b344e9f4e0c25)

BibTeX

@misc{dc39dfcf80b4ea,
  title = {Quality over Quantity: Diversity-Aware Data Selection for Efficient Verilog Code Generation — arXiv:2609.22765},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.22765},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_39dfcf80b4ea; sha256 d7031dc228ffa5c0c428052f6d1af80e0af76279db2b8ad4a60b344e9f4e0c25},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.22765. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Large Language Models (LLMs) have shown remarkable potential in Verilog code generation, yet existing datasets contain con siderable noise and redundancy. Prior data selection methods address only isolated quality aspects, neglect the global diversity of the training set, and cannot capture Verilog-specific structural semantics. To bridge this gap, we propose VeriSelector, the first data selection framework for Verilog code generation that jointly optimizes quality and diversity. We formulate the selec tion problem as a constrained bi-objective subset selection problem and solve it via a three-stage approximation. For quality, a multi-granularity pipeline first verifies functional correctness through testbench simulation and then filters misaligned samples via Instruction-Following Difficulty (IFD) scoring. For diversity, 109-dimensional Verilog-specific structural features (AST, CFG, and Netlist) are fused with textual embeddings for clustering-based diversity modeling. A proportional adaptive sampling strat egy then allocates per-cluster quotas guided by IFD ranks, with a provable distribution preservation guarantee. Experiments on three LLMs and three benchmarks show that VeriSelector outperforms full-dataset training and state-of-the-art baselines us ing only 20%-25% of the data, achieving Performance Retention Rates above 118% and reducing training time by over 80%. Notably, VeriSelector improves average Pass@1 by 18.49%-29.43% over full-dataset training and by 1.36%-7.95% over the best-performing baseline across all evaluated models.

Tema degli esploratori: calcolo e infrastrutture (retro-catalogo arXiv: annunci del 2026-09-22 in cs.AR).
Categorie arXiv: Hardware Architecture (cs.AR).
Pagina del lavoro: https://arxiv.org/abs/2609.22765 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-09T06:50:43.567Z dalla pagina https://arxiv.org/catchup/cs.AR/2026-09-22?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).