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TreeGraft: Adaptive Multi-Drafter Grafting for Tree-Based Speculative Decoding
Author1, Author2, Author3, Author4, Author5
speculative decodingmulti-draftertree-structured methodslanguage models
2608.26112
Builder Relevance
1h ago80%
Abstract
TreeGraft introduces a multi-drafter framework that enhances speculative decoding in large language models by improving the quality of draft trees while managing drafting costs.
Reality Card
Core Claim
TreeGraft outperforms fixed single-drafter strategies by an average of 15.1%, with a maximum gain of 26.6% across various benchmarks.
Method / Result
Achieved a 15.1% average improvement over single-drafter strategies.
Limitations
The reliance on a lightweight scheduler may limit generalizability across different model architectures.
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