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Multi-Token Prediction via Self-Distillation

Researchers have developed a novel self-distillation technique to accelerate language model inference. This method transforms a standard autoregressive model into a faster multi-token predictor without needing auxiliary models or complex inference pipelines. The resulting model achieves over threefold speedup in decoding with a minimal accuracy drop on benchmarks like GSM8K. AI

影响 Enables faster deployment of existing language models by improving inference efficiency without architectural changes.

排序理由 Academic paper detailing a new method for accelerating language model inference.

在 arXiv cs.CL 阅读 →

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Multi-Token Prediction via Self-Distillation

报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · John Kirchenbauer, Abhimanyu Hans, Brian Bartoldson, Micah Goldblum, Ashwinee Panda, Tom Goldstein ·

    Multi-Token Prediction via Self-Distillation

    arXiv:2602.06019v2 Announce Type: replace Abstract: Existing techniques for accelerating language model inference, such as speculative decoding, require training auxiliary speculator models and building and deploying complex inference pipelines. We consider a new approach for con…