Ant Group's UBTree improves speculative decoding: parallel screening of draft trees enables large models to verify more tokens at a time.
57 minutes ago
Insight Beating AI News Flash: Ant Group’s Bailing has open-sourced the large language model speculative decoding scheme UBTree. Speculative decoding lets a small model draft outputs in advance, with a large model validating multiple tokens at once to cut down on token-by-token generation computation rounds. However, drafts suffer from hit rate issues: the earlier the small model guesses wrong, the less content can be adopted directly later. Existing tech prepares multiple candidate paths, organizes them into a draft tree, and prunes low-probability branches. UBTree improves the filtering method: it first parallelly predicts possible tokens for subsequent positions, then uses a lightweight model to assess adjacent token logical coherence. Different combinations are scored simultaneously, eliminating repeated model runs along each path. It then selects promising branches to form a draft tree for the large model’s validation. The team also adjusted the filtering model’s training approach, enabling it to not only focus on highest-probability answers but also identify viable alternative paths, letting the large model accept more tokens per round. In seven Qwen3-4B tests, the average speedup ratio using the DFlash 2 selector for tree construction was 5.95x. After switching to the UBTree selector and adjusting training, this rose to 6.88x, an approximate 16% improvement over the original scheme.
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