SpecBlock achieves 8-13% higher mean speedup than EAGLE-3 at 44-52% drafting cost via block-iterative drafting with hidden-state inheritance, dynamic rank-head branching, valid-prefix masking, and optional cost-aware bandit adaptation.
Recurrent drafter for fast speculative decoding in large language models
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SnapKV selects clustered important KV positions per attention head from an observation window at the prompt end, yielding 3.6x faster generation and 8.2x better memory efficiency on 16K-token inputs with comparable performance across 16 datasets.
D-PACE derives per-position weights from a surrogate of expected accepted draft length to shift training focus toward currently limiting positions, yielding measured gains in wall-clock speedup and emitted length across benchmarks.
citing papers explorer
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SpecBlock: Block-Iterative Speculative Decoding with Dynamic Tree Drafting
SpecBlock achieves 8-13% higher mean speedup than EAGLE-3 at 44-52% drafting cost via block-iterative drafting with hidden-state inheritance, dynamic rank-head branching, valid-prefix masking, and optional cost-aware bandit adaptation.
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SnapKV: LLM Knows What You are Looking for Before Generation
SnapKV selects clustered important KV positions per attention head from an observation window at the prompt end, yielding 3.6x faster generation and 8.2x better memory efficiency on 16K-token inputs with comparable performance across 16 datasets.
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D-PACE: Dynamic Position-Aware Cross-Entropy for Parallel Speculative Drafting
D-PACE derives per-position weights from a surrogate of expected accepted draft length to shift training focus toward currently limiting positions, yielding measured gains in wall-clock speedup and emitted length across benchmarks.