Papers by Siddhartha Jain

3 papers
Scaling Test-Time Compute to Achieve IOI Gold Medal with Open-Weight Models (2026.acl-long)

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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.
Lightweight reranking for language model generations (2024.acl-long)

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Challenge: Large Language Models (LLMs) can exhibit considerable variation in quality of sampled outputs.
Approach: They propose a method for reranking LLM generations using pairwise statistics . they show strong improvements for selecting the best k generations for code generation tasks .
Outcome: The proposed approach improves selection and generation quality for code generation tasks and autoformalization, summarization, and translation tasks.
Reasoning in Token Economies: Budget-Aware Evaluation of LLM Reasoning Strategies (2024.emnlp-main)

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Challenge: Existing evaluations that focus on performance metrics miss a key factor: increased effectiveness due to additional compute.
Approach: They propose to incorporate the compute budget into evaluations to provide a more informative comparison that takes into account both performance metrics and computational cost.
Outcome: The proposed framework outperforms reasoning strategies when they use comparable compute resources.

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