Revisiting the Effectiveness of LLM Pruning for Test-Time Scaling 事件

Revisiting the Effectiveness of LLM Pruning for Test-Time Scaling arXiv:2604.25098v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) now exhibit remarkable reasoning capabilities through test-time compute scaling (TTS), with impressive performance across math and coding benchmarks. In parallel, research in model compression has developed pruning methods that seek to remove redundant/detrimental parameters without sacrificing task performance. The intersection of these two re