Papers by Sean Narenthiran
Scaling Test-Time Compute to Achieve IOI Gold Medal with Open-Weight Models (2026.acl-long)
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Mehrzad Samadi, Aleksander Ficek, Sean Narenthiran, Siddhartha Jain, Wasi Uddin Ahmad, Somshubra Majumdar, Vahid Noroozi, Boris Ginsburg
| Challenge: | Competitive programming has become a rigorous benchmark for evaluating the reasoning and problem-solving capabilities of large language models (LLMs). |
| Approach: | They propose a scalable and reproducible test-time compute framework that achieves IOI gold-level performance using open-weight models. |
| Outcome: | The proposed framework achieves IOI gold-level performance using open-weight models . it scales consistently with available compute, narrowing the gap between open and closed systems. |
Genetic Instruct: Scaling up Synthetic Generation of Coding Instructions for Large Language Models (2025.acl-industry)
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Somshubra Majumdar, Vahid Noroozi, Mehrzad Samadi, Sean Narenthiran, Aleksander Ficek, Wasi Uddin Ahmad, Jocelyn Huang, Jagadeesh Balam, Boris Ginsburg
| Challenge: | Large Language Models (LLMs) require high quality instruction data for effective alignment, especially in code generation tasks where expert curated datasets are expensive to produce. |
| Approach: | They propose a scalable algorithm for synthesizing large-scale, high quality coding instructions using evolutionary principles. |
| Outcome: | The proposed approach generates 7.5 million coding instructions with a small seed population and is highly parallelizable and effective even with weaker generator models. |