SP-DocReader: Difference-Aware Self-Play for Precise Document OCR

arXiv:2610.11148 · cs.CV, cs.AI · Submitted 2026-10-08 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-10-08

Updated: 2026-10-08

Code: https://github.com/huggingface/accelerate

Terminology

• Benchmark — 15× in this paper · explained in 2 episodes
• Alignment — 12× in this paper · explained in 1 episode
• Fine-Tuning — 12× in this paper · explained in 5 episodes
• Adaptation — 8× in this paper · explained in 1 episode
• Fidelity — 8× in this paper · explained in 2 episodes
• Focused Fidelity Loss — 8× in this paper
• Reading Discrepancy Masking — 6× in this paper
• Ablation — 4× in this paper · explained in 1 episode
• Accuracy — 4× in this paper · explained in 1 episode
• Generalization — 4× in this paper · explained in 13 episodes
• Instruction Tuning — 4× in this paper · explained in 2 episodes
• Longest Common Subsequence — 4× in this paper
• Character Error Rate — 3× in this paper
• Provenance — 3× in this paper · explained in 4 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Adam — 2× in this paper · explained in 2 episodes
• Direct Preference Optimization — 2× in this paper
• Embeddings — 2× in this paper · explained in 2 episodes
• Multimodal Inputs — 2× in this paper · explained in 2 episodes
• Verification — 2× in this paper · explained in 2 episodes
• Zero-Shot Generalization — 2× in this paper · explained in 5 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Catastrophic Forgetting — 1× in this paper · explained in 5 episodes
• Direct Preference Optimization (DPO) — 1× in this paper · explained in 9 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Supervised Fine-Tuning (SFT) — 1× in this paper · explained in 8 episodes
• Test-time Scaling — 1× in this paper · explained in 2 episodes
• Tokenization — 1× in this paper · explained in 2 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes
• Wasserstein Distance — 1× in this paper · explained in 4 episodes

Sources

• ScreenAI: A Vision-Language Model for UI and Infographics Understanding
• Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond
• Qwen3-VL Technical Report
• PaliGemma: A versatile 3B VLM for transfer
• Nougat: Neural Optical Understanding for Academic Documents
• Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling
• How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites
• RLHF Workflow: From Reward Modeling to Online RLHF
• PP-OCR: A Practical Ultra Lightweight OCR System
• OCRBench v2: An Improved Benchmark for Evaluating Large Multimodal Models on Visual Text Localization and Reasoning
• LLaVA-OneVision: Easy Visual Task Transfer
• Mono-InternVL: Pushing the Boundaries of Monolithic Multimodal Large Language Models with Endogenous Visual Pre-training
• An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning
• OmniDocBench: Benchmarking Diverse PDF Document Parsing with Comprehensive Annotations
• Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences
• MinerU: An Open-Source Solution for Precise Document Content Extraction
• InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency
• Layout and Task Aware Instruction Prompt for Zero-shot Document Image Question Answering
• SPACE: Noise Contrastive Estimation Stabilizes Self-Play Fine-Tuning for Large Language Models
• Vary: Scaling up the Vision Vocabulary for Large Vision-Language Models

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