Trees from Marginals: Autoregressive drafting with factorized priors
The authors introduced Weaver, a lightweight adapter that builds proposal trees from top-K predictions of a factorized drafter to restore conditional dependencies. This hybrid approach achieves a 4.37x speedup over autoregressive decoding by using the factorized model's marginals as priors while avoiding expensive full-vocabulary projections.