arXiv:2606.29223v1 Announce Type: new Abstract: Autoregressive LLM decoding evaluates every generated token through the full layer stack, even though many tokens become predictable at intermediate depths. Existing lossless depth-adaptive methods exploit this redundancy by choosing a single non-final exit depth and verifying its prediction with the final-depth model. However, our measurements show that this selection-based strategy leaves substantial headroom: choosing an exit too late wastes computation, while choosing one too early triggers fallback and discards dependent drafts. We propose D
The continuous growth in LLM model size and computational demands makes efficiency optimization a critical and immediate research focus.
Improving LLM decoding efficiency directly impacts inference cost, speed, and accessibility, which are key bottlenecks for broader AI adoption and scaling.
This research suggests a more nuanced approach to LLM decoding, potentially moving beyond single-exit depth methods to more adaptive, multi-depth strategies, thereby optimizing computational resource utilization.
- · LLM developers and researchers
- · Cloud AI service providers
- · Applications reliant on real-time LLM inference
- · Less efficient LLM decoding researchers
- · Generic compute hardware
More efficient and faster outputs from large language models, reducing inference costs.
Lower operational costs for deploying and scaling LLM-powered applications, leading to wider adoption and new use cases.
Increased competition in the LLM service market as efficiency gains democratize access to advanced AI capabilities.
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Read at arXiv cs.LG