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Pan Xu

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#machine learning Preprint Oct 2026

Decoupled Marginal Sharpening for Training-Free Inference-Time Scaling

Decoupled Marginal Sharpening (DMS) is proposed, which separately controls how strongly the model favors particular reasoning paths and how strongly it favors final answers after aggregating support across those paths.

Wei-Xin Wang, Wei Deng, Anderson Schneider et al. · 0 citations
#artificial intelligence Preprint Sep 2026

Extremely Sparse Supervision Incentivizes Reasoning Ability

Overall, the results challenge the assumption that effective post-training must be token-intensive and point to a new direction for understanding and designing more efficient post-training algorithms.

Zhi-Shuai Liu, Xingzi Xu, Mehmet Saygin Seyfioglu et al. · 4 citations

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