# Supplementary Materials and Visualizations

This repository contains the Appendix and extensive qualitative results for the paper **"SoliReward: Mitigating Susceptibility to Reward Hacking and Annotation Noise in Video Generation Reward Models"**.

We provide visual evidence validating the efficacy of our proposed **SoliReward** method and the **BT-WT (Bradley-Terry with Win-Tie)** objective applied to the HunyuanVideo backbone.

## 📖 Usage Instructions

* **Browser Recommendation:** Please use **Google Chrome** to view the HTML files. Other browsers may not support the video playback formats or layout correctly.
* **Loading Time:** The pages contain multiple videos. Please allow a few seconds for all assets to load completely.

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## 📂 Visualization Contents

### 1. Method Comparison (SOTA vs. Ours)
**File Path:** `html_VideoAlign_SoliReward(Ours)/index.html`

This module presents a side-by-side comparison of video generation quality after post-training the **HunyuanVideo** backbone using different reward models.

* **Baseline:** The original, unoptimized HunyuanVideo output.
* **VideoAlignMQ:** The model optimized using the VideoAlign Reward (MQ).
* **SoliReward (Ours):** The model optimized using our proposed SoliReward.

### 2. Ablation Study: Impact of Training Objectives (BT vs. BT-WT)
**File Path:** `html_BT_BT-WT(ours)/index.html`

This part validates the effectiveness of our proposed loss function strategy. It demonstrates the performance gains achieved by explicitly incorporating "Win-Tie" pairs into the training process.

* **Baseline:** The original HunyuanVideo output.
* **BT Reward Model:** Trained using the standard Bradley-Terry model (utilizing only Win-Lose pairs).
* **BT-WT Reward Model (Ours):** Trained using our proposed **Bradley-Terry with Win-Tie** strategy (utilizing both Win-Lose and Win-Tie pairs).