TreeSpark: Learning to Draft the Trees You Verify for Speculative Decoding
Read the original on arXiv Computation and Language →TreeSpark is a new training and inference framework that extends the semi‑autoregressive DSpark architecture to improve speculative decoding. It builds parent‑conditioned candidate trees in a single backbone pass and then replays their expansion, pruning, and packing during training, using survival negative log‑likelihood and a capped coverage hinge to weight retained edges and promote target‑important alternatives. Evaluations on Qwen3‑4B and Qwen3‑8B across nine general benchmarks and six writing domains show that TreeSpark increases mean acceptance length by 23.4%–31.6% and mean single‑request throughput by 15.1%–25.0% compared to DSpark, with additional gains when using the proposed supervision. whyItMatters":"TreeSpark demonstrates that a unified training and inference approach can substantially boost both acceptance length and throughput in speculative decoding, offering measurable performance improvements for large language models."
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