The Diminishing Returns of Early-Exit Decoding in Modern LLMs
In Large Language Model (LLM) inference, early-exit refers to stopping computation at an intermediate layer once the prediction is sufficiently confident, thereby reducing latency and cost. However, recent LLMs adopt improved pretraining recipes and architectures that reduce layer redundancy, potentially limiting early-exit opportunities. We re-evaluate layer-wise early-exit in modern LLMs and analyze how intermediate representations evolve during training. We introduce a metric to quantify a model's intrinsic suitability for early-exit and propose a benchmark for researchers to explore the potential early-exit benefits on different models and workloads. Our results show a diminishing tre...
| Authors | Rui Wei, Rui Du, Hanfei Yu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 24 March 2026 |
| Last updated at source | 24 March 2026 |
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