Cut Your Losses! Learning to Prune Paths Early for Efficient Parallel Reasoning
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 their signal source (internal vs. external) and learnability (learnable vs. non-learnable). This classification reveals the unexplored potential of learnable internal methods, motivating our proposal of STOP (Super TOken for Pruning). Extensive evaluations across LRMs ranging from 1.5B to 20B parameters demonstrate that STOP ach...
| Authors | Jiaxi Bi, Tongxu Luo, Wenyu Du et al. |
|---|---|
| Primary category | cs.CL |
| Published | 17 April 2026 |
| Last updated at source | 2 June 2026 |
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