Why Do Reasoning Models Lose Coverage? The Role of Data and Forks in the Road
Recent progress in large language models has led to the emergence of reasoning models, which have shown strong performance on complex tasks through specialized fine-tuning procedures. While these methods reliably improve pass@1 accuracy, prior works have observed that they show a coverage shrinkage behavior, where pass@k degrades relative to the base model. In this paper, we investigate the cause of reasoning shrinkage under SFT-based post-training. We hypothesize that this behavior is driven by properties of the fine-tuning data, specifically related to decision points or "forks in the road" scenarios where model encounters indecipherable patterns with multiple valid reasoning paths. To ...
| Authors | Ngoc-Hieu Nguyen, Parshin Shojaee, Phuc Minh Nguyen et al. |
|---|---|
| Primary category | cs.LG |
| Published | 16 May 2026 |
| Last updated at source | 1 September 2026 |
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