Papers with partial reasoning
Confidence Leaps in LLM Reasoning: Early Stopping and Cross-Model Transfer (2026.eacl-short)
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| Challenge: | Large Language Models build confidence gradually during reasoning, but internal dynamics of how confidence evolves during this reasoning process remain poorly understood. |
| Approach: | They propose a model-agnostic early-stopping heuristic that halts generation upon detecting a "confidence leap" they argue that conviction is often reached in a discrete "moment of insight" they propose to train models without sacrificing accuracy . |
| Outcome: | The proposed model-agnostic heuristic reduces generation time without sacrificing accuracy and significantly reduces the generation time. |
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. |