Speculative decoding (SD) accelerates large language model inference by reducing the sequential cost of token-by-token generation. Advanced SD methods reduce proposal cost with parallel drafters that
SpecOPD accelerates speculative decoding by parallelizing on-policy draft supervision across independent branches, improving acceptance length and training throughput across diverse model architectures and tasks.
Speculative decoding (SD) accelerates large language model inference by reducing the sequential cost of token-by-token generation. Advanced SD methods reduce proposal cost with parallel drafters that predict entire blocks in one forward pass, but overall speedup still depends on acceptance length. On-policy distillation (OPD) can push well-trained drafters beyond acceptance-length plateaus, yet conventional pipelines collect supervision through sequential draft-and-verify rollouts. Our key insight is that on-policy supervision need not inherit these dependencies: a draft block can be supervised using only its reference prefix and draft-sampled history. We propose SpecOPD, a branch-parallel…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
SpecOPD: Branch-Parallel On-Policy Distillation for Speculative Decoding was first seen on Hugging Face · Daily papers on Nov 5, 2026. Since then it has appeared in 14 checks on 2 different days. Its best position was #13 of 30, reached on Nov 5, 21:28 UTC. It dropped off the list after Nov 6, 05:28 UTC.
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#13 on Hugging Face · Daily papers, on Nov 5, 21:28 UTC. Trendoday checks this list every 120 minutes.
It was first seen on Nov 5, 2026 and has been on the list on 2 days (14 checks) since.