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Speculative decoding

Token Recycling

Superseded baseline#14 of 151 most-superseded

Superseded — cited as a baseline and beaten by newer methods

3 papers critique it · 5 beat it on benchmarks

What papers say

Verbatim critique sentences, each from a paper that cites Token Recycling as a baseline.

Despite producing non-uniform trees, these methods optimize within a single draft source, so the quality differences they exploit remain within-source.
— Goose: Anisotropic Speculation Trees for Training-Free Speculative Decoding
“Notably, Token Recycle's performance remains flat despite increasing trajectories, unlike other model-free approaches. This limitation likely comes from its lookup table update strategy, which replaces rather than aggregates information from new trajectories.”
— Accelerated Test-Time Scaling with Model-Free Speculative Sampling
“On the other head, model-free retrieval-based drafters, such as CopySpec~copyspec and Token Recycling~tokenrecycle, offer training-free and lightweight alternatives but suffer from limited retrieval quality.”
— When, What, and How: Rethinking Retrieval-Enhanced Speculative Decoding

Beaten on benchmarks

Head-to-head results where a newer method reports beating Token Recycling. Values are copied from the source paper's tables — verify against the cited paper.

What to use instead

Recent methods in the same sub-problem, not yet superseded in the knowledge base — arXiv benchmark leaders, not vetted production recommendations.