Papers with GPQA Diamond
The Reasoning Lingua Franca: A Double-Edged Sword for Multilingual AI (2026.eacl-short)
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| Challenge: | Large Reasoning Models (LRMs) are highly effective on mathematical, scientific, and other question-answering tasks. |
| Approach: | They compare an LRM's reasoning in English to that of a multilingual question . they find that English reasoning traces exhibit a substantially higher presence of cognitive behaviors . |
| Outcome: | The LRMs generate reasoning sequences in English, but the language of the question is not. |
Z1: Efficient Test-time Scaling with Code (2025.emnlp-industry)
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| Challenge: | Large Language Models (LLMs) can achieve enhanced complex problem-solving through test-time computing scaling, but this often entails longer contexts and numerous reasoning token costs. |
| Approach: | They propose an efficient test-time scaling method that trains LLMs on code-related reasoning trajectories and a novel Shifted Thinking Window to mitigate overthinking overhead. |
| Outcome: | The proposed method reduces overthinking overhead while maintaining performance. |
Long Chain-of-Thought Fine-tuning via Understanding-to-Reasoning Transition (2025.emnlp-main)
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Chenxin An, Zhihui Xie, Xiaonan Li, Ming Zhong, Shansan Gong, Lei Li, Jun Zhang, Jingjing Xu, Lingpeng Kong
| Challenge: | Existing research on long-context scaling in language models has focused on managing lengthy input prompts instead of producing long outputs. |
| Approach: | They propose a sequence-level curriculum learning framework that shifts a model’s focus from interpreting long chain-of-thoughts to generating them. |
| Outcome: | Experiments on rigorous reasoning benchmarks, including AIME24 and GPQA Diamond, show that the proposed approach surpasses standard fine-tuning by over 10% while maintaining robust performance on understanding tasks. |