Papers with early-exit mechanisms

3 papers
Accelerating BERT Inference for Sequence Labeling via Early-Exit (2021.acl-long)

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Challenge: Existing early-exit mechanisms are designed for sequence-level tasks, rather than sequence labeling.
Approach: They propose to extend sentence-level early-exit to accelerate inference of PTMs . they propose a token-level mechanism that allows partial tokens to exit early at different layers .
Outcome: The proposed approach can save up to 66%75% inference cost with minimal performance degradation.
LEAP: Layer-wise Exit-Aware Pretraining for Efficient Transformer Inference (2026.acl-industry)

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Challenge: Layer-aligned distillation and convergence-based early exit are dominant computational efficiency paradigms for transformer inference.
Approach: They propose a training objective that aligns intermediate student layers to teacher representations and reconciles this incompatibility with standard distillation.
Outcome: The proposed model achieves 1.61 measured wall-clock speedup with 91.9% of samples exiting by layer 7 and 1.80 theoretical layer reduction, where standard distilled models achieve zero effective speedup.
Runaway is Ashamed, But Helpful: On the Early-Exit Behavior of Large Language Model-based Agents in Embodied Environments (2025.findings-emnlp)

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Challenge: Experiments with 4 different LLMs across 5 embodied environments show significant efficiency improvements, with only minor drops in agent performance.
Approach: They propose an intrinsic method that injects exit instructions during generation and an extransic system that verifies task completion to determine when to halt an agent’s trial.
Outcome: The proposed method injects exit instructions during generation and an exit method verifies task completion to determine when to halt an agent’s trial.

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