Cut Your Losses! Learning to Prune Paths Early for Efficient Parallel Reasoning 事件

Cut Your Losses! Learning to Prune Paths Early for Efficient Parallel Reasoning arXiv:2604.16029v2 Announce Type: replace Abstract: Parallel reasoning enhances Large Reasoning Models (LRMs) but incurs prohibitive costs due to futile paths caused by early errors. To mitigate this, path pruning at the prefix level is essential, yet existing research remains fragmented without a standardized framework. In this work, we propose the first systematic taxonomy of path pruning, categorizing methods by