Method Drift›Speculative decoding
SSD
Speculative decoding
superseded — cited as a baseline and beaten by newer methods
2 papers critique it · 1 beat it on benchmarks
What papers say
Verbatim critique sentences, each from a paper that cites SSD as a baseline.
“Some approaches integrate DLLMs with speculative decoding~campbell2025self,cheng2025deerdraftdiffusionverify; however, such designs introduce autoregressive decoding behavior into DLLM inference, thereby undermining the distinctive non-autoregressive reasoning characteristics of DLLMs.”
— Factorization-Error-Free Discrete Diffusion Language Model via Speculative Decoding“SSD instead increases acceptance by adding a constant probability bias, enabling long-tail sampling but ignoring acoustic similarity and risking erroneous acceptances.”
— Principled Coarse-Grained Acceptance for Speculative Decoding in Speech
Beaten on benchmarks
Head-to-head results where a newer method reports beating SSD. Values are copied from the source paper's tables — verify against the cited paper.
- Factorization-Error-Free Discrete Diffusion Language Model via Speculative Decoding
FeF-DLLM (step=2) beats SSD · Acc. [GSM8K]
79.38 vs 77.10
- Factorization-Error-Free Discrete Diffusion Language Model via Speculative Decoding
FeF-DLLM (step=2) beats SSD · Acc. [MATH]
36.40 vs 34.94
- Factorization-Error-Free Discrete Diffusion Language Model via Speculative Decoding
FeF-DLLM (step=2) beats SSD · Acc. [HumanEval]
48.78 vs 43.09
- Factorization-Error-Free Discrete Diffusion Language Model via Speculative Decoding
FeF-DLLM (step=2) beats SSD · Acc. [MBPP]
42.60 vs 39.20
- Factorization-Error-Free Discrete Diffusion Language Model via Speculative Decoding
FeF-DLLM (step=2) beats SSD · Acc. [Mean]
51.79 vs 48.58
- Factorization-Error-Free Discrete Diffusion Language Model via Speculative Decoding
FeF-DLLM (step=4) beats SSD · Acc. [GSM8K]
79.68 vs 77.10
- Factorization-Error-Free Discrete Diffusion Language Model via Speculative Decoding
FeF-DLLM (step=4) beats SSD · Acc. [MATH]
36.56 vs 34.94
- Factorization-Error-Free Discrete Diffusion Language Model via Speculative Decoding
FeF-DLLM (step=4) beats SSD · Acc. [HumanEval]
49.39 vs 43.09
- Factorization-Error-Free Discrete Diffusion Language Model via Speculative Decoding
FeF-DLLM (step=4) beats SSD · Acc. [MBPP]
42.60 vs 39.20
- Factorization-Error-Free Discrete Diffusion Language Model via Speculative Decoding
FeF-DLLM (step=4) beats SSD · Acc. [Mean]
52.06 vs 48.58
Newer alternatives
Recent methods in the same sub-problem, not yet superseded in the knowledge base.
- May 14, 2026
- Apr 6, 2026
- Principled Coarse-Graining (PCG)Principled Coarse-Grained Acceptance for Speculative Decoding in SpeechNov 5, 2025