PrePair: Speculative Drafter Training for Generative UI under Semantic Acceptance

Generative UI lets large language models answer with interactive interfaces instead of plain text, but an interface spans many more tokens than a text reply, limiting real-time interaction. Speculative decoding accelerates generation by letting a small drafter propose tokens that a large target verifies in parallel, keeping each only if it matches the target's token distribution. In Generative UI, however, we find that the drafter writes many components that are semantically correct but not token-identical to the target's, and some correct ones that the target gets wrong, so per-token acceptance both discards valid drafts and bounds the whole system by the target. In this paper, we lift speculative decoding from per-token acceptance to component-level semantic acceptance, in which the target checks all components against several criteria in one forward pass. Existing drafter training, built for per-token acceptance, imitates the target, which under semantic acceptance both rewrites components the drafter already writes correctly and passes on the target's errors. To overcome this, we propose PrePair, which reinforces accepted components and distills the target only on the divergent tokens of failed ones, located by chain repair, so the two objectives never share a token. With a 1.7B drafter and a 32B target, PrePair raises semantic acceptance over supervised fine-tuning from 81.0% to 87.8%, for a 3.5× end-to-end speedup over the target alone versus 2.3× for EAGLE-3, and on Macaron-A2UI the trained system outscores the 32B target writing directly under an independent evaluator.