Papers with code-related reasoning trajectories
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. |