GlimpRouter: Efficient Collaborative Inference by Glimpsing One Token of Thoughts
A recent publication on arXiv (2601.05110v3) presents GlimpRouter, a technique aimed at enhancing collaborative inference in Large Reasoning Models (LRMs). While LRMs are capable of producing intricate multi-step reasoning, they often face issues with high latency and costs. Collaborative inference seeks to balance tasks between smaller and larger models, yet determining the appropriate model for each task is complex. Current routing methods depend on token probabilities or retrospective validation, which can introduce extra overhead. GlimpRouter suggests that the complexity of a reasoning task can be gauged from the entropy of its initial token, drawing inspiration from the 'Aha Moment' in LRMs, ultimately minimizing inference overhead by utilizing the first token as an indicator.
Key facts
- Paper arXiv:2601.05110v3 introduces GlimpRouter.
- GlimpRouter addresses collaborative inference for Large Reasoning Models (LRMs).
- LRMs generate multi-step chains of thought but have high latency and cost.
- Collaborative inference selectively allocates work between lightweight and large models.
- Existing routing strategies use local token probabilities or post-hoc verification.
- GlimpRouter infers step difficulty from the first token's entropy.
- Inspired by the 'Aha Moment' phenomenon in LRMs.
- GlimpRouter reduces inference overhead.
Entities
Institutions
- arXiv