FireRed-OCR-2B: Solving Table and LaTeX Hallucinations with GRPO

FireRedTeam Releases FireRed-OCR-2B Utilizing GRPO to Solve Structural Hallucinations in Tables and LaTeX for Software Developers

FireRedTeam has released FireRed-OCR-2B, an end-to-end vision-language model designed specifically to treat document parsing as a structural engineering task. The model achieves a state-of-the-art 92.94% score on the OmniDocBench v1.5 benchmark, outperforming significantly larger models like Qwen2-VL-72B and Gemini-1.5-Pro. This release marks a significant shift from traditional multi-stage OCR pipelines to unified transformer architectures.

Why This Matters

Document digitization frequently suffers from ‘structural hallucinations’ where Large Vision-Language Models (LVLMs) invent formulas or fail to close hierarchical tags in complex tables. For developers, these errors break downstream tasks like RAG (Retrieval-Augmented Generation) and data analysis, as disordered rows and invalid LaTeX syntax require manual correction that negates the benefits of automation.

FireRed-OCR-2B addresses this by moving beyond simple text generation to enforce syntactic validity through reinforcement learning. By eliminating the need for separate detection and recognition models, it reduces system complexity and inference latency while maintaining robustness against ‘long-tail’ layouts such as non-standard legal forms and academic papers with overlapping figures.

Key Insights

• Format-Constrained GRPO (Group Relative Policy Optimization) rewards the model for maintaining syntactic validity, ensuring LaTeX formulas and table tags are logically closed.
• FireRed-OCR-2B achieved a 92.94% overall score on OmniDocBench v1.5, surpassing DeepSeek-OCR 2 (91.09%) and Gemini-1.5-Pro (90.33%).
• The model architecture is built on the Qwen2-VL-2B-Instruct foundation, utilizing a specialized three-stage Progressive Training Pipeline: Multi-task Pre-alignment, Specialized SFT, and GRPO.
• A ‘Geometry + Semantics’ Data Factory uses geometric feature clustering to synthesize balanced datasets, enabling better handling of non-standard layouts compared to traditional systems like PaddleOCR.
• The use of GRPO eliminates the need for a separate ‘critic’ model, streamlining the training process to focus specifically on high-friction document parsing areas.

Practical Applications

• Production RAG Environments: Implementing FireRed-OCR-2B as a single-model solution to reduce inference latency and architectural complexity. Pitfall: Relying on multi-stage pipeline systems often leads to layout detection failures on dense technical PDFs.
• Academic and Legal Document Parsing: Converting complex multi-column papers and non-standard forms into structured Markdown. Pitfall: Treating document parsing as ‘impressionist’ text generation leads to mathematically invalid LaTeX and broken table hierarchies.

References:

• https://www.marktechpost.com/2026/03/01/fireredteam-releases-firered-ocr-2b-utilizing-grpo-to-solve-structural-hallucinations-in-tables-and-latex-for-software-developers/