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Chenrui Fan

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#artificial intelligence Preprint Sep 2026

Learning to Stop without Learning to Stop: Self-Supervised Confidence Training Improves Reasoning Efficiency

Self-supervised confidence fine-tuning makes reasoning more efficient, reducing generated tokens by up to 25\% at matched accuracy across Gemma, Qwen, Nemotron, and GPT-OSS models on mathematical, scientific, and coding reasoning benchmarks, with efficiency gains comparable to methods that explicitly optimize for short...

Parsa Hosseini, Akasha Tigalappanavara, Sumit Nawathe et al. · 0 citations

Preprint Aug 2026

Thinking Hard, Not Smart: Reasoning Models Fail to Ration Test-Time Compute Across Questions

An exam-style evaluation framework is introduced for studying the global budget allocation of reasoning language models when multiple problems share an end-to-end cost or latency constraint, in which a model must distribute one shared token budget across questions with different difficulty and point values.

Chenrui Fan, Yize Cheng, Ming Li et al. · 0 citations