DEX replaces single-depth selection with parallel exploration over multiple candidate depths, committing the final-depth token while collapsing reusable states to reduce per-token computation.
Speculative Speculative Decoding
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
abstract
Autoregressive decoding is bottlenecked by its sequential nature. Speculative decoding has become a standard way to accelerate inference by using a fast draft model to predict upcoming tokens from a slower target model, and then verifying them in parallel with a single target model forward pass. However, speculative decoding itself relies on a sequential dependence between speculation and verification. We introduce speculative speculative decoding (SSD) to parallelize these operations. While a verification is ongoing, the draft model predicts likely verification outcomes and prepares speculations pre-emptively for them. If the actual verification outcome is then in the predicted set, a speculation can be returned immediately, eliminating drafting overhead entirely. We identify three key challenges presented by speculative speculative decoding, and suggest principled methods to solve each. The result is Saguaro, an optimized SSD algorithm. Our implementation is on average 30% faster than optimized speculative decoding baselines and up to 5x faster than autoregressive decoding with open source inference engines.
years
2026 3verdicts
UNVERDICTED 3representative citing papers
K-Forcing introduces progressive self-forcing distillation to train a conditional push-forward model that jointly decodes k future tokens per forward pass, yielding 2.4-3.5x speedup at k=4 with modest quality loss on LM1B and OpenWebText.
EntMTP is a training-free entropy-guided scheduler for multi-token prediction that dynamically selects from task-specific Pareto-optimal trees to accelerate LLM inference by up to 1.36x on benchmarks without quality loss.
citing papers explorer
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Depth Exploration for LLM Decoding
DEX replaces single-depth selection with parallel exploration over multiple candidate depths, committing the final-depth token while collapsing reusable states to reduce per-token computation.
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K-Forcing: Joint Next-K-Token Decoding via Push-Forward Language Modeling
K-Forcing introduces progressive self-forcing distillation to train a conditional push-forward model that jointly decodes k future tokens per forward pass, yielding 2.4-3.5x speedup at k=4 with modest quality loss on LM1B and OpenWebText.
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EntMTP: Accelerating LLM Inference with Entropy Guided Multi Token Prediction
EntMTP is a training-free entropy-guided scheduler for multi-token prediction that dynamically selects from task-specific Pareto-optimal trees to accelerate LLM inference by up to 1.36x on benchmarks without quality loss.