# D-REAL

Speculative decoding drafters are usually trained with token-level cross-entropy, yet at fixed overhead end-to-end speedup is governed by acceptance length, the number of draft tokens the target model accepts consecutively. D-REAL writes that acceptance length as an expectation along the drafter's own sampled trajectories and builds a differentiable surrogate from acceptance ratios with stop-gradient denominators, giving an unbiased gradient estimator. Across ten benchmarks on the parallel drafters DSpark and DFlash2, one post-training epoch raises macro-average acceptance length from 5.07 to 5.13 and from 4.88 to 5.10; in concurrent GSM8K serving, DFlash2 output throughput rises 9.4%.

Speculative decoding drafters are usually trained with token-level cross-entropy, yet at fixed overhead end-to-end speedup is governed by acceptance length, the number of draft tokens the target model accepts consecutively. D-REAL writes that acceptance length as an expectation along the drafter's own sampled trajectories and builds a differentiable surrogate from acceptance ratios with stop-gradient denominators, giving an unbiased gradient estimator. Across ten benchmarks on the parallel drafters DSpark and DFlash2, one post-training epoch raises macro-average acceptance length from 5.07 to 5.13 and from 4.88 to 5.10; in concurrent GSM8K serving, DFlash2 output throughput rises 9.4%.