better usage demo in readme (#477)

* better usage demo in readme

* example of non-default configs

README.md

@@ -38,11 +38,9 @@ pip install reasoning-gym
 
 _Note that this project is currently under active development, and the version published on PyPI may be a few days behind `main`._
 
-## 🛠️ Development
+## ✨ Quickstart
 
-For development setup, see [CONTRIBUTING.md](CONTRIBUTING.md#development-setup).
-
-## ✨ Example Usage
+Starting to generate tasks using Reasoning Gym is straightforward:
 
 ```python
 import reasoning_gym
@@ -65,6 +63,26 @@ metadata: {'animals': {'sheep': 2, 'dog': 2}, 'total_legs': 16}
 ...
 ```
 
+Use keyword arguments to pass task-specific configuration values:
+
+```python
+reasoning_gym.create_dataset('leg_counting', size=10, seed=42, max_animals=20)
+```
+
+Create a composite dataset containing multiple task types, with optional relative task weightings:
+
+```python
+from reasoning_gym.composite import DatasetSpec
+specs = [
+    # here, leg_counting tasks will make up two thirds of tasks
+    DatasetSpec(name='leg_counting', weight=2, config={}),  # default config
+    DatasetSpec(name='figlet_font', weight=1, config={"min_word_len": 4, "max_word_len": 6}),  # specify config
+]
+reasoning_gym.create_dataset('composite', size=10, seed=42, datasets=specs)
+```
+
+For the simplest way to get started training models with Reasoning Gym, we recommend using the `verifiers` library, which directly supports RG tasks. See `examples/verifiers` for details. However, RG data can be used with any major RL training framework.
+
 ## 🔍 Evaluation
 
 Instructions for running the evaluation scripts are provided in [eval/README.md](https://github.com/open-thought/reasoning-gym/blob/main/eval/README.md).