Diffusion Drafts, AR Verifies: Accelerating Document OCR with Self-Speculative Decoding
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Merged summary
TL;DR - GravityOCR combines diffusion-based parallel drafting with autoregressive verification in one parameter-shared OCR model. It accelerates document decoding while retaining accuracy close to the original GLM-OCR model.
- The model drafts multiple tokens in parallel, then causally verifies them before commitment to reduce errors.
- Its autoregressive path supports GRPO using sequence- and structure-level OCR rewards without diffusion-trajectory likelihood estimation.
- On OmniDocBench v1.6, GRPO improved the Overall score from 94.92 to 95.16; the final model scored near GLM-OCR’s 95.48.
- In SGLang, GravityOCR committed 9.7 tokens per forward pass on average, delivering 3.94Ă— decode-only and 1.32Ă— end-to-end speedups over autoregressive decoding.
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Diffusion Drafts, AR Verifies: Accelerating Document OCR with Self-Speculative Decoding
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Semantic Scholar citations 0 · Semantic Scholar influential citations 0
TL;DR - GravityOCR combines diffusion-based parallel drafting with autoregressive verification in one parameter-shared OCR model. It accelerates document decoding while retaining accuracy close to the original GLM-OCR model.
- The model drafts multiple tokens in parallel, then causally verifies them before commitment to reduce errors.
- Its autoregressive path supports GRPO using sequence- and structure-level OCR rewards without diffusion-trajectory likelihood estimation.
- On OmniDocBench v1.6, GRPO improved the Overall score from 94.92 to 95.16; the final model scored near GLM-OCR’s 95.48.
- In SGLang, GravityOCR committed 9.7 tokens per forward pass on average, delivering 3.94Ă— decode-only and 1.32Ă— end-to-end speedups over autoregressive decoding.