# Technical Document Analysis: Multimodal Math Problem-Solving Reward Models ## Overview The image presents a comparative analysis of three reward models for evaluating multimodal math problem-solving processes. The diagram uses flowcharts with labeled components, directional arrows, and annotations to illustrate differences in reward mechanisms and correction capabilities. --- ## Diagram Components ### (a) Outcome Reward Model **Title**: Outcome Reward Model **Key Components**: - **Question** (Input: Math equation "E=mc²") - **Output** (Intermediate processing step) - **Answer** (Final response) - **ORM** (Robot icon with "Reward" arrow) **Flow**: ``` Question → Output → Answer → Reward (only for final answers) ``` **Annotations**: - Red text: "ONLY Reward Final Ans" - ORM robot has a smiley face --- ### (b) Multimodal Process Reward Model **Title**: Multimodal Process Reward Model **Key Components**: - **Multimodal Math Qns** (Input: Math problems with equations and diagrams) - **Steps 1, 2, ..., T** (Intermediate reasoning steps) - **Answer** (Final response) - **PRM** (Robot icon with "Reward" arrow) **Flow**: ``` Multimodal Math Qns → Step 1 → Step 2 → ... → Step T → Answer → Reward ``` **Annotations**: - Red text box: "Limited Explainability" - Red text box: "No Correction Mechanism" - PRM robot has a neutral expression --- ### (c) GM-PRM (Ours) **Title**: GM-PRM (Ours) **Key Components**: - **Multimodal Math Qns** (Input: Math problems with equations and diagrams) - **Steps 1, 2, ..., T** (Intermediate reasoning steps) - **1st incorrect step** (Highlighted with dashed arrow) - **After Correction** (Dashed arrow indicating refinement) - **Refined BoN** (Refined version of the answer) - **GM-PRM** (Robot icon with "Reward" arrow) **Flow**: ``` Multimodal Math Qns → Step 1 → Step 2 → ... → Step T → Answer → Reward ``` **Sub-process**: ``` Analysis & Judgement → Step Intent → Image Alignment → Reasoning Logic ``` **Annotations**: - Red text: "Refined & Corrected Version" - Dashed arrows indicate iterative refinement - GM-PRM robot has a smiling face with cheeks --- ## Key Differences Between Models | Feature | Outcome Reward Model (a) | Multimodal Process Reward Model (b) | GM-PRM (c) | |------------------------|--------------------------|-------------------------------------|--------------------------------| | **Reward Scope** | Final answer only | All steps | All steps + corrections | | **Explainability** | Not specified | Limited | Improved through analysis | | **Correction** | No | No | Yes (via "After Correction") | | **Robot Icon** | ORM (smiley) | PRM (neutral) | GM-PRM (smiling with cheeks) | --- ## Spatial Grounding & Component Isolation 1. **Header**: Each section title (a, b, c) is bolded and positioned at the top of its respective flowchart. 2. **Main Chart**: - Components are arranged left-to-right with vertical stacking for sub-processes (e.g., "Analysis & Judgement" in c). - Arrows indicate sequential flow (solid for primary flow, dashed for corrections). 3. **Footer**: No explicit footer, but annotations are placed in red text boxes near relevant components. --- ## Trend Verification - **No numerical data** present; trends are represented through structural differences in component connectivity and annotation emphasis. --- ## Conclusion The diagram demonstrates a progression from simple outcome-based evaluation (a) to more sophisticated process-aware models (b and c). The proposed GM-PRM (c) introduces explicit correction mechanisms and multi-stage analysis, addressing limitations in explainability and adaptability observed in prior models.