Paper: HedgeSpec Enables Provably No-Regret Drafter Selection for LLMs
D3VAUX · x · 2026-07-31
A joint team from Rice University and AWS published Not-a-Bandit, proposing a new algorithm called HedgeSpec for online draft model selection in speculative decoding for LLM inference.
Key Highlights:
- Zero Extra Queries for Evaluation: The algorithm accurately evaluates all draft models without incurring additional queries to the target model, competing with the best draft model in hindsight.
- Exponential Improvement: It improves exponentially over existing bandit-based approaches as the number of draft models increases.
- Broad Applicability: Applicable to any speculative decoding method (single draft, multi-drafts, draft-trees) with system-efficient versions that substantially reduce computation and latency overhead.
- Results: Extensive experiments on open-source LLMs show HedgeSpec significantly outperforms state-of-the-art baselines like EAGLE3 and BanditSpec, particularly when domain-expert drafters are available and long reasoning chains are required.
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