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DTSTAMP:20260730T152640Z
LOCATION:Exhibit Hall
DTSTART;TZID=America/Los_Angeles:20260728T171300
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UID:dac_DAC 2026_sess306_WIP3242@linklings.com
SUMMARY:Late Breaking Results: Influential Data Selection for LLM-based RT
 L Generation
DESCRIPTION:Zhan Song (University of Maryland, College Park) and Cunxi Yu 
 (University of Maryland/NVIDIA)\n\nScarcity and noise in open-source datas
 ets severely limit Large Language Models (LLMs) in Register Transfer Level
  (RTL) design. To address this, we propose a targeted data selection frame
 work using Low-rank Gradient Similarity Search (LESS). By leveraging gradi
 ent-based influence estimation, LESS filters detrimental data by selecting
  training examples that align with the target task's gradient trajectory. 
 Experiments show that fine-tuning on just 5% of LESS-selected data matches
  full-dataset training performance. Furthermore, using LESS-selected data 
 for second-stage fine-tuning outperforms fully trained models, whereas ran
 dom selection degrades them. Prioritizing data quality over quantity thus 
 offers a promising path to state-of-the-art automated hardware design.\n\n
 Track: Student\n\n
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