Breaking the Resource Monopoly: LLM Post-Training and Serving with Modest Data and Compute
Abstract
My research focuses on making LLM training and serving widely accessible to academia and smaller organizations by reducing dependence on proprietary data and heavy compute. In this talk, I will present a coherent framework that unifies data curation without labels, zero-data self-evolution, reward calibration for reliable confidence estimation, and computation-efficient language model inference.
Fields of Study & Subjects
Topic Modeling
Natural Language Processing Techniques
Domain Adaptation and Few-Shot Learning
Publication details
- Journal / source
- Underline Science Inc.
- Publication date
- 7 January 2026
- Language
- EN