👁 PARD

| Paper | Github |

Introduction

PARD is a high-performance speculative decoding method that also enables low-cost adaptation of autoregressive draft models into parallel draft models. PARD-2 further advances PARD by introducing a Target-Aligned Parallel Draft Model for dual-mode speculative decoding. Instead of optimizing draft models only for token-level prediction accuracy, PARD-2 aligns draft-model training with the inference-time objective of maximizing consecutive token acceptance. PARD-2 offers the following advantages:

• Target-Aligned Optimization: PARD-2 reformulates the draft-model objective from next-token prediction accuracy to acceptance-length optimization, better matching the draft-then-verify process used during speculative decoding.

• Confidence-Adaptive Token Optimization: PARD-2 introduces Confidence-Adaptive Token (CAT) optimization, which adaptively reweights tokens according to their contribution to the verification process. This improves the alignment between draft generation and target-model acceptance.

• Dual-Mode Speculative Decoding: A single PARD-2 draft model supports both target-independent and target-dependent modes, combining the deployment flexibility of PARD with the stronger alignment capability of target-aware methods.

State-of-the-Art Performance: Across diverse models and tasks, PARD-2 achieves up to 6.94× lossless acceleration. On LLaMA3.1-8B, PARD-2 surpasses EAGLE-3 by 1.9× and PARD by 1.3×, setting a new performance frontier for speculative decoding.

👁 Image

Throughput and Latency Trade-offs on vLLM. PARD-2 consistently achieves a superior Pareto frontier across various batch sizes from 1 to 64.

Model Weights

How To Use

Please visit PARD2 repo for more information

Citation

@article{an2026pard,
 title={PARD-2: Target-Aligned Parallel Draft Model for Dual-Mode Speculative Decoding},
 author={An, Zihao and Liu, Taichi and Liu, Ziqiong and Li, Dong and Liu, Ruofeng and Barsoum, Emad},
 journal={arXiv preprint arXiv:2605.08632},
 year={2026}
}

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