Do Dynamic Routers Need Memory? HeRo: History-Aware Routing for Efficient LLM Inference
Dynamic layer routing reduces the inference cost of Large Language Models (LLMs) by learning to skip layers for individual tokens. Existing methods, however, treat each routing decision as a local operation conditioned solely on the current hidden state which is a formulation that overlooks the sequential, path-dependent nature of routing across depth: earlier decisions shape the representations seen by downstream routers, and the layer-usage objective couples all decisions jointly. We propose History-Aware Routing (HeRo), a dynamic routing framework that resolves this mismatch by introducing a router memory mechanism to maintain an explicit routing state across model depth. The memory is...
| Authors | Hongjin Lin, Wentao Wan, Keze Wang |
|---|---|
| Primary category | cs.AI |
| Published | 8 September 2026 |
| Last updated at source | 8 September 2026 |
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