Update src/about.py

src/about.py

@@ -32,10 +32,12 @@ INTRODUCTION_TEXT = """
 # ... (rest of your about.py content) ...
 LLM_BENCHMARKS_TEXT = """
 ## MLE-Dojo
-MLE-Dojo, a Gym-style framework for systematically training, evaluating, and improving autonomous large language model (LLM) agents in iterative machine learning engineering (MLE) workflows. 
-MLE-Dojo provides an interactive environment enabling agents to iteratively experiment, debug, and refine solutions through structured feedback loops. Built upon 200+ real-world Kaggle challenges (e.g., tabular data analysis, computer vision, natural language processing, and time series forecasting). MLE-Dojo covers diverse, open-ended MLE tasks carefully curated to reflect realistic engineering scenarios such as data processing, architecture search, hyperparameter tuning, and code debugging.
-Its fully executable environment supports comprehensive agent training via both supervised fine-tuning and reinforcement learning, facilitating iterative experimentation, realistic data sampling, and real-time outcome verification.
-
+MLE-Dojo is a Gym-style framework for systematically training, evaluating, and improving autonomous large language model (LLM) agents in 
+iterative machine learning engineering (MLE) workflows. Built upon 200+ real-world Kaggle challenges. MLE-Dojo covers diverse, 
+open-ended MLE tasks carefully curated to reflect realistic Machine Learning Engineering scenarios such as data processing, 
+architecture search, hyperparameter tuning, and code debugging, etc. MLE-Dojo's fully executable environment and flexible 
+interface support comprehensive agent training via both supervised fine-tuning and reinforcement learning, facilitating 
+iterative experimentation, realistic data sampling, and real-time outcome verification.
 ## New Updates
 We actively maintain this as a long-term real-time leaderboard with updated models and evaluation tasks to foster community-driven innovation.
 """