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Shaul Druckmann

2 accepted papers

2025

Informed Correctors for Discrete Diffusion Models

NeurIPS 2025poster

Discrete diffusion has emerged as a powerful framework for generative modeling in discrete domains, yet efficiently sampling from these models remains challenging. Existing sampling strategies often struggle to balance computation and sample quality when the number of sampling steps is reduced, even…

Cited by 0SourceScholar
2025

Rethinking Fine-Tuning when Scaling Test-Time Compute: Limiting Confidence Improves Mathematical Reasoning

NeurIPS 2025poster

Recent progress in large language models (LLMs) highlights the power of scaling test-time compute to achieve strong performance on complex tasks, such as mathematical reasoning and code generation. This raises a critical question: how should model training be modified to optimize performance under a…

Cited by 0SourcecodeScholar