Update README.md

README.md

@@ -64,17 +64,6 @@ dParallel-LLaDA-Distill Dataset</a></td>
   </tbody>
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-## 🔥Updates
-* 🔥 **[Oct 2, 2025]**: Our arxiv paper is available.
-* 🔥 **[Oct 1, 2025]**: Code, model and dataset are released.
-
-## 🔧  Installation:
-
-```bash
-conda create -n dparallel python==3.10
-conda activate dparallel
-pip3 install -r requirements.txt
-```
 
 ## 🚀 Quick Start:
 ```python
@@ -100,27 +89,6 @@ print("Response:",tokenizer.batch_decode(out[0][:, input_ids.shape[1]:], skip_sp
 print("NFE:",out[1])
 ```
 
-
-## 🔥 Training
-### 1. Certainty-Forcing Distillation with LoRA:
-We provide training scripts for our proposed Certainty-Forcing Distillation process. The implementation utilizes LoRA during the training process, with the configuration details specified in [config_lora_llada.yaml](https://github.com/czg1225/dParallel/blob/master/configs/config_lora_llada.yaml).
-```bash
-deepspeed --master_port 29501 --include localhost:0,1,2,3 llada_train.py
-```
-
-### 2. LoRA Merge:
-After training, merge the LoRA weights to get the dParallel-dLLM.
-```bash
-python merge_lora.py
-```
-
-## ⚡ Evaluation:
-We provide evaluation scripts for the GSM8K, Minerva_MATH, HumanEval, and MBPP benchmarks. Although our approach does not rely on caching or sparse attention techniques, it is fully compatible with them and can achieve even greater speedups when combined.
-```bash
-sh eval.sh
-```
-
-
 ## 📖 Experimental Results
 ### Results on LLaDA-8B-Instruct:
 ![llada-exp](assets/llada_exp.png)
@@ -131,9 +99,6 @@ sh eval.sh
 ### Better Speed-Accuracy Trade-off:
 ![trade-off](assets/trade-off.png)
 
-## ☀️ Acknowledgement
-Our code builds on [LLaDA](https://github.com/ML-GSAI/LLaDA), [Dream](https://github.com/DreamLM/Dream), [Fast-dLLM](https://github.com/NVlabs/Fast-dLLM/tree/main), and [dKV-Cache](https://github.com/horseee/dkv-cache), and we acknowledge these great works for laying the groundwork that made our approach possible.
-
 ## Citation
 If our research assists your work, please give us a star ⭐ or cite us using:
 ```