Papers by Shangyin Tan
S*: Test Time Scaling for Code Generation (2025.findings-emnlp)
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Dacheng Li, Shiyi Cao, Chengkun Cao, Xiuyu Li, Shangyin Tan, Kurt Keutzer, Jiarong Xing, Joseph E. Gonzalez, Ion Stoica
| Challenge: | S* is the first hybrid test-time scaling framework that significantly improves the coverage and selection accuracy of generated code. |
| Approach: | They propose a hybrid test-time scaling framework that augments parallel scaling with sequential scaling to further increase the performance. |
| Outcome: | The proposed framework outperforms existing scaling approaches in large-scale modeling and reasoning models. |
SlimFit: Memory-Efficient Fine-Tuning of Transformer-based Models Using Training Dynamics (2024.naacl-long)
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Arash Ardakani, Altan Haan, Shangyin Tan, Doru Thom Popovici, Alvin Cheung, Costin Iancu, Koushik Sen
| Challenge: | SlimFit reduces the memory requirements of transformer-based models by analyzing their training dynamics and freezing less-contributory layers during fine-tuning. |
| Approach: | They propose a tool that analyzes transformer-based models and freezes less-contributory layers during fine-tuning to reduce the overall on-device memory usage. |
| Outcome: | SlimFit reduces the memory requirements of transformer-based models by analyzing their training dynamics and freezing less-contributory layers during fine-tuning. |
LangProBe: a Language Program Benchmark (2025.findings-emnlp)
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Shangyin Tan, Lakshya A Agrawal, Arnav Singhvi, Liheng Lai, Michael J Ryan, Dan Klein, Omar Khattab, Koushik Sen, Matei Zaharia
| Challenge: | Composing language models into multi-step language programs is a mainstream paradigm for building AI systems, but tradeoffs in this space have only scarcely been studied before. |
| Approach: | They propose a benchmarking tool to evaluate the architectures and optimization strategies for language programs . they find that optimized language programs offer strong cost-quality Pareto improvement . |
| Outcome: | The proposed framework evaluates the impact of program architectures and optimizers on quality and cost. |