Papers with DeepPrune
DeepPrune: Parallel Scaling without Inter-trace Redundancy (2026.findings-acl)
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| Challenge: | Parallel scaling is a powerful paradigm to enhance reasoning capabilities in large language models. |
| Approach: | They propose a framework that enables efficient parallel scaling through dynamic pruning. |
| Outcome: | The proposed framework achieves token reductions of 65.73% to 88.50% compared to consensus sampling while maintaining competitive accuracy within 3.4 percentage points. |