Lu Yin

Layer pruning has emerged as a widely adopted technique for improving the efficiency of large language models (LLMs). Although multiple recent works report little-to-no accuracy loss on general knowledge tasks, their effect on long-chain reasoning, a more brittle yet crucial capability, remains largely unexplored. Overlooking this can yield deceptively ''lossless pruning'' that causes catastrophic failures on real-world reasoning workloads. In this work, we study the impact of layer pruning on long-chain reasoning through the lens of test-time scaling, a key mechanism in modern LLMs that enables strong reasoning capacity by allocating more computation at inference time. With extensive experiments, we demonstrate that pruning even one or two layers can severely impair test-time scaling, with performance collapsing drastically on long reasoning benchmarks even when performance on knowledge-intensive tasks remains stable. Furthermore, we find that standard supervised fine-tuning remedies fail to recover test-time scaling once it has deteriorated. Through in-depth analyses, we identify the mechanisms underlying this fragility of test-time scaling and highlight the fundamental risks of applying layer pruning to reasoning-intensive LLMs. Our findings challenge the prevailing ''lossless pruning'' narrative, call for more sensitive evaluation protocols, and provide insights for designing pruning strategies that preserve robust reasoning.