Variational Speculative Decoding: Rethinking Draft Training from Token Likelihood to Sequence Acceptance
arXiv:2602.05774v4 Announce Type: replace Abstract: Speculative decoding accelerates inference for (M)LLMs, yet a training-decoding discrepancy persists: while existing methods optimize single greedy trajectories, decoding involves verifying and ranking multiple sampled draft paths. We propose Variational Speculative Decoding (VSD), formulating draft training as variational inference over latent proposals (draft paths). VSD maximizes the marginal probability of target-model acceptance, yielding an ELBO that promotes high-quality latent proposals while minimizing divergence from the target dist
The rapid advancement of LLMs necessitates more efficient inference methods to reduce computational costs and improve user experience, making innovations in decoding strategies highly relevant.
This research could significantly improve the speed and efficiency of large language models, lowering the bar for deployment and potentially enabling new applications.
Decoding for M/LLMs could become significantly faster and more robust, moving beyond single greedy trajectories to more sophisticated verification of sampled paths.
- · AI model developers
- · Cloud providers (reduced compute costs)
- · Enterprises deploying LLMs
- · End-users of LLM applications
Faster inference speeds will reduce the cost of running large language models.
Lower inference costs could enable broader adoption of LLMs in diverse applications and services.
Increased efficiency might shift competitive advantages towards entities with superior decoding and fine-tuning methodologies, rather than just raw model size.
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Read at arXiv cs.LG