Update README.md

README.md

@@ -175,34 +175,6 @@ response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
 print(response)
 ```
 
-## 🔥 Training
-### 1. Training with LoRA:
-We provide training scripts for our proposed supervised verification fine-tuning approach. The implementation utilizes LoRA during the training process, with the configuration details specified in [config_lora_r1_7b.yaml](https://github.com/czg1225/VeriThinker/blob/main/config/config_lora_r1_7b.yaml).
-```bash
-deepspeed --include localhost:0,1,2,3,4,5,6,7 train_svft.py
-```
-
-### 2. LoRA Merge:
-After training, merge the LoRA weights to get the reasoning model.
-```bash
-python merge_lora.py
-```
-
-## ⚡ Evaluation:
-We provide evaluation scripts for three mathematical datasets: MATH500, AIME 2024, and AIME 2025. Our implementation leverages the [vLLM](https://docs.vllm.ai/en/latest/) framework to ensure efficient inference during evaluation.
-
-### 1. Evaluation on MATH500 Dataset
-```bash
-CUDA_VISIBLE_DEVICES=0,1,2,3 python eval_math500.py
-```
-### 2. Evaluation on AIME 2024 Dataset
-```bash
-CUDA_VISIBLE_DEVICES=0,1,2,3 python eval_aime24.py
-```
-### 3. Evaluation on AIME 2025 Dataset
-```bash
-CUDA_VISIBLE_DEVICES=0,1,2,3 python eval_aime25.py
-```
 
 ## 📖 Experimental Results
 ### CoT Compression Results: