Update README.md

README.md

@@ -12,7 +12,7 @@ pipeline_tag: audio-text-to-text
 ## Introduction
 
 R1-AQA is a audio question answering (AQA) model based on `Qwen2-Audio-7B-Instruct`, optimized through reinforcement learning using the group relative policy optimization (GRPO) algorithm.
-This implementation has achieved state-of-the-art performance on MMAU *Test-mini* benchmark with only 38k post-training samples.
+This implementation has achieved state-of-the-art performance on the MMAU benchmark with only 38k post-training samples.
 For more details, please refer to our [Github](https://github.com/xiaomi-research/r1-aqa) and [Technical Report](https://arxiv.org/abs/2503.11197).
 
 Our main findings are as follows:
@@ -27,7 +27,7 @@ Additional Notes:
 - The AVQA training set originally consists of approximately 40k samples. However, we use only about 38k samples because some data sources have become invalid. Other datasets using YouTube sources face a similar issue, such as AudioSet. We believe that the missing 2k samples do not have a significant impact on the training results.
 - The statement about the 8.2B parameters is based on the *Qwen2-Audio Technical Report*.
 
-### Table: Accuracies (%) on MMAU benchmark
+### Table: Accuracies (%) on the MMAU benchmark
 
 | Model                                 | Method                | Test-mini | Test  | Test-mini | Test  | Test-mini | Test  | Test-mini | Test  |
 |---------------------------------------|-----------------------|-----------|-------|-----------|-------|-----------|------|------------|-------|
@@ -36,21 +36,18 @@ Additional Notes:
 | Audio Flamingo 2                      | Direct Inference\*    | 61.56     | 65.10 | 73.95     | 72.90 | 30.93     | 40.26 | 55.48     | 59.42 |
 | GPT4o + Strong Cap.                   | Direct Inference\*    | 57.35     | 55.83 | 49.70     | 51.73 | 64.86     | 68.66 | 57.30     | 58.74 |
 | Llama-3-8B-Instruct + Strong Cap.     | Direct Inference\*    | 50.75     | 49.10 | 48.93     | 48.93 | 55.25     | 62.70 | 52.10     | 53.57 |
-| Gemini Pro v1.5                       | Direct Inference\*    | 56.75     | 54.46 | 49.40     | 48.56 | 58.55     | 55.90 | 54.90     | 52.97 |
 | Qwen2-Audio-7B-Instruct               | Direct Inference\*    | 54.95     | 45.90 | 50.98     | 53.26 | 42.04     | 45.90 | 49.20     | 52.50 |
-| GPT4o + weak cap.                     | Direct Inference\*    | 39.33     | 35.80 | 41.90     | 39.52 | 58.25     | 68.27 | 45.70     | 48.65 |
-| Llama-3-8B-Instruct + Weak Cap.       | Direct Inference\*    | 34.23     | 33.73 | 38.02     | 42.36 | 54.05     | 61.54 | 42.10     | 45.87 |
 | SALAMONN                              | Direct Inference\*    | 41.00     | 40.30 | 34.80     | 33.76 | 25.50     | 24.24 | 33.70     | 32.77 |
 | Qwen2-Audio-7B-Instruct               | CoTA \[1\]            | 60.06     | -     | 64.30     | -     | 60.70     | -     | 61.71     | -     |
 | Qwen2-Audio-7B-Instruct               | Zero-Shot-CoT \[2\]   | 61.86     | -     | 56.29     | -     | 55.26     | -     | 57.80     | -     |
-| **Qwen2-Audio-7B-Instruct**           | **Ours 1️⃣**           | 69.37     | -     | 66.77     | -     | 57.36     | -     | 64.50     | -     |
-| **Qwen2-Audio-7B-Instruct**           | **Ours 2️⃣**           | 68.77     | 69.76 | 64.37     | 61.40 | 63.66     | 62.70 | 65.60     | 64.36 |
+| **Qwen2-Audio-7B-Instruct**           | **GRPO (Ours) 1️⃣**    | 69.37     | -     | 66.77     | -     | 57.36     | -     | 64.50     | -     |
+| **Qwen2-Audio-7B-Instruct**           | **GRPO (Ours) 2️⃣**    | 68.77     | 69.76 | 64.37     | 61.40 | 63.66     | 62.70 | 65.60     | 64.36 |
 
 #### Notes
 
 1️⃣ It is the original model, identical to the one on Hugging Face and described in our technical report.  
 2️⃣ It is the model submitted to [EvalAI](https://eval.ai/web/challenges/challenge-page/2391/overview) for evaluation, trained multiple times to achieve balanced results. (**The results on the [leaderboard](https://sakshi113.github.io/mmau_homepage/#leaderboard) contain some typographical errors, and we are currently in communication with the organizers to correct them.**)  
-\* The data are sourced from the [MMAU official website](https://sakshi113.github.io/mmau_homepage/)   
+\* The data are sourced from the [MMAU official website](https://sakshi113.github.io/mmau_homepage/).   
 \[1\] Xie, Zhifei, et al. "Audio-Reasoner: Improving Reasoning Capability in Large Audio Language Models." arXiv preprint arXiv:2503.02318 (2025).  
 \[2\] Ma, Ziyang, et al. "Audio-CoT: Exploring Chain-of-Thought Reasoning in Large Audio Language Model." arXiv preprint arXiv:2501.07246 (2025).