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CLLMs: Consistency Large Language Models

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arxiv 2403.00835 v4 pith:KMXFMSIX submitted 2024-02-28 cs.CL cs.AI

Siqi Kou , Lanxiang Hu , Zhezhi He , Zhijie Deng , Hao Zhang This is my paper · ORCID
classification cs.CLcs.AI
keywords decodingjacobifixedgenerationpointstatetimesaccomplished
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abstract

Parallel decoding methods such as Jacobi decoding show promise for more efficient LLM inference as it breaks the sequential nature of the LLM decoding process and transforms it into parallelizable computation. However, in practice, it achieves little speedup compared to traditional autoregressive (AR) decoding, primarily because Jacobi decoding seldom accurately predicts more than one token in a single fixed-point iteration step. To address this, we develop a new approach aimed at realizing fast convergence from any state to the fixed point on a Jacobi trajectory. This is accomplished by refining the target LLM to consistently predict the fixed point given any state as input. Extensive experiments demonstrate the effectiveness of our method, showing 2.4$\times$ to 3.4$\times$ improvements in generation speed while preserving generation quality across both domain-specific and open-domain benchmarks.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. xPress: Parallel Refinement for Diffusion Drafters in Speculative Decoding

    cs.AI 2026-08 conditional novelty 6.0 of 10

    xPress restores causality to diffusion-drafter drafts with a lightweight refiner solved by a few parallel Jacobi iterations, lifting acceptance length ~30% and throughput ~1.3x over dFlash on Qwen3-8B.

  2. UniCMs: A Unified Consistency Model For Efficient Multimodal Generation and Understanding

    cs.CV 2025-02 conditional novelty 6.0 of 10

    UniCMs applies consistency distillation to a unified multimodal transformer, treating image mask-diffusion steps and text parallel-decoding steps as one shared denoising trajectory, enabling 2 to 8 step generation and...

  3. CITER: Collaborative Inference for Efficient Large Language Model Decoding with Token-Level Routing

    cs.CL 2025-02 conditional novelty 5.0 of 10

    CITER trains a token-level router with preference optimization to route non-critical tokens to a small model and critical tokens to a large model, reducing inference cost on QA and math benchmarks.

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