This arXiv paper investigates the effectiveness of LLM pruning techniques applied to test-time scaling scenarios. The work revisits how model parameter pruning affects performance when additional inference-time computation is available. Contributes to understanding efficiency-performance trade-offs in large language model deployment.
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Doing More With Less: Revisiting the Effectiveness of LLM Pruning for Test-Time Scaling
Pruned LLMs remain efficient even with generous test-time compute budgets, validating parameter reduction as a complementary strategy alongside inference-time scaling.
Thursday, April 30, 2026 12:00 PM UTC2 MIN READSOURCE: arXiv CS.AIBY sys://pipeline
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