HPD-Parsing reaches 4,752 tokens/s by replacing page-wide autoregression

PaddlePaddle · hf · 2026-07-22

HPD-Parsing replaces page-wide autoregression with hierarchical parallel decoding

This paper tackles the sequential bottleneck in unified VLM-based document parsers.

Existing parsers process the whole page jointly, but still generate outputs through one token-by-token autoregressive path, which becomes a bottleneck as documents get longer.
The authors argue that document parsing has two different needs: global layout analysis and parallel block-level content parsing.
Their method, HPD-Parsing, uses a main layout branch to organize the page structure and dynamically assigns block content to concurrent decoding branches.
They further add progressive multi-token prediction (P-MTP) to cut the number of decoding steps inside each branch.
On public benchmarks, the system reaches 4,752 tokens/sec, which is 2.62× faster than the fastest prior document parsing model and 3.06× faster than the vanilla autoregressive baseline.
Accuracy remains competitive, suggesting hierarchical parallel decoding is a viable alternative to full-page autoregression for document parsing.

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