Papers by Xiuyu Li
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. |
LLoCO: Learning Long Contexts Offline (2024.emnlp-main)
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Sijun Tan, Xiuyu Li, Shishir G Patil, Ziyang Wu, Tianjun Zhang, Kurt Keutzer, Joseph Gonzalez, Raluca Popa
| Challenge: | Large language models are still unable to handle long contexts due to the quadratic computational and memory overhead of the self-attention mechanism and the substantial KV cache sizes during generation. |
| Approach: | They propose a method to learn contexts offline through context compression and in-domain parameter-efficient finetuning with LoRA. |
| Outcome: | The proposed model outperforms in-context learning while using 30 fewer tokens during inference and significantly reduces the cost of long document question answering. |