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-# RobustTests
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+<h2><strong>Robust Code RL via Faulty-Code-Driven Test Case Synthesis and Dense Reward Shaping</strong></h2>
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+[![arXiv](https://img.shields.io/badge/arXiv-2608.24135-b31b1b.svg?logo=arxiv)](https://arxiv.org/abs/2608.24135)
+[![Venue](https://img.shields.io/badge/Venue-Findings%20of%20EMNLP%202026-4c6ef5.svg)](https://arxiv.org/abs/2608.24135)
+[![Dataset License](https://img.shields.io/badge/Dataset%20License-CC--BY--4.0-f1df61.svg)](https://creativecommons.org/licenses/by/4.0/)
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+</div>
 
 ## Dataset Description
 
 RobustTests is a high-quality test case dataset specifically designed for **reinforcement learning from verifiable rewards (RLVR)** in code generation tasks. It addresses the fundamental limitation of insufficient test coverage that often causes **false positives** and **reward hacking** in RL-based code training.
 
-📄 **Paper**: [Robust Code RL via Faulty-Code-Driven Test Case Synthesis and Dense Reward Shaping](https://arxiv.org/abs/2608.24135) — Findings of EMNLP 2026
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 **Important**: To avoid copyright issues, this dataset only provides the test case collections — it does not include the original problem descriptions. Each problem is identified by its `id` and `source`, which can be used to match with the corresponding problems in [Code-Contests-Plus](https://huggingface.co/datasets/ByteDance-Seed/Code-Contests-Plus) or the original competitive programming platforms.
 
 ## Key Features