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RTPrune boosts DeepSeek-OCR inference speed by 1.23x with novel token pruning

By PulseAugur Editorial · Summary by gemini-2.5-flash-lite from 2 sources

Researchers have developed RTPrune, a novel two-stage token pruning method designed to enhance the efficiency of DeepSeek-OCR inference. This method mimics the model's two-stage reading process, first prioritizing high-norm tokens for salient information and then merging remaining tokens using optimal transport theory. RTPrune also incorporates a dynamic pruning ratio tailored for OCR tasks, achieving a superior balance between accuracy and efficiency. AI

Summary written by gemini-2.5-flash-lite from 2 sources. How we write summaries →

IMPACT Improves inference speed and efficiency for OCR tasks, potentially reducing computational costs for processing long documents.

RANK_REASON This is a research paper detailing a new method for optimizing inference efficiency in an existing OCR model.

Read on arXiv cs.CV →

COVERAGE [2]

  1. arXiv cs.CV TIER_1 · Ben Wan, Yan Feng, Zihan Tang, Weizhe Huang, Yuting Zeng, Jia Wang, Tongxuan Liu ·

    RTPrune: Reading-Twice Inspired Token Pruning for Efficient DeepSeek-OCR Inference

    arXiv:2605.00392v1 Announce Type: new Abstract: DeepSeek-OCR leverages visual-text compression to reduce long-text processing costs and accelerate inference, yet visual tokens remain prone to redundant textual and structural information. Moreover, current token pruning methods fo…

  2. arXiv cs.CV TIER_1 · Tongxuan Liu ·

    RTPrune: Reading-Twice Inspired Token Pruning for Efficient DeepSeek-OCR Inference

    DeepSeek-OCR leverages visual-text compression to reduce long-text processing costs and accelerate inference, yet visual tokens remain prone to redundant textual and structural information. Moreover, current token pruning methods for conventional vision-language models (VLMs) fai…

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