Papers by Ion Stoica
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. |
Language Models Can Easily Learn to Reason from Demonstrations (2025.findings-emnlp)
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Dacheng Li, Shiyi Cao, Tyler Griggs, Shu Liu, Xiangxi Mo, Eric Tang, Sumanth Hegde, Kourosh Hakhamaneshi, Shishir G Patil, Matei Zaharia, Joseph E. Gonzalez, Ion Stoica
| Challenge: | Large reasoning models (LRMs) tackle complex problems by following long chain-of-thoughts (Long CoT) however, the training techniques and data requirements to elicit Long CoT remain poorly understood. |
| Approach: | They propose to use data-efficient supervised fine-tuning and parameter-efficient low-rank adaptation to elicit Long CoT reasoning. |
| Outcome: | The proposed model can learn Long CoT reasoning through data-efficient supervised fine-tuning and parameter-efficient low-rank adaptation. |
Contrastive Code Representation Learning (2021.emnlp-main)
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| Challenge: | Recent work learns contextual representations of source code by reconstructing tokens from their context. |
| Approach: | They propose a contrastive pre-training task that learns code functionality, not form . they propose scalable compilers that can generate variants of a program . |
| Outcome: | The proposed task outperforms RoBERTa on an adversarial code clone detection benchmark by 39% AUROC. |
Grounded Graph Decoding improves Compositional Generalization in Question Answering (2021.findings-emnlp)
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| Challenge: | Current compositional generalization models lose syntax context when learning a flat input . a new method to improve compositional globalization is proposed to ground structured predictions with an attention mechanism. |
| Approach: | They propose a method to ground structured predictions by a structure-based attention mechanism. |
| Outcome: | The proposed method performs competitively on the Compositional Freebase Questions dataset. |