LiteStage: Latency-aware Layer Skipping for Multi-Stage Reasoning

ACL ARR 2026 January Submission9507 Authors

Keywords: Multi-step Reasoning, Small Language Model, Layer Skip, Generation Early Exit

Abstract: Multi-stage reasoning has emerged as an effective strategy for enhancing the reasoning capability of small language models by decomposing complex problems into sequential sub-stages. However, this comes at the cost of increased latency. We observe that existing adaptive acceleration techniques, such as layer skipping, struggle to balance efficiency and accuracy in this setting due to two key challenges: (1) stage-wise variation in skip sensitivity, and (2) the generation of redundant output tokens. To address these, we propose LiteStage, a latency-aware layer skipping framework for multi-stage reasoning. LiteStage combines a stage-wise offline search that allocates optimal layer budgets with an online confidence-based generation early exit to suppress unnecessary decoding. Experiments on three benchmarks, e.g., OBQA, CSQA, and StrategyQA, show that LiteStage outperforms prior training-free layer skipping methods.

Paper Type: Long

Research Area: LLM Efficiency

Research Area Keywords: NLP in resource-constrained settings, Reasoning, Layer Skip, LLM Efficiency

Contribution Types: NLP engineering experiment, Approaches to low-resource settings, Approaches low compute settings-efficiency

Languages Studied: English

Submission Number: 9507