Add reinforcement-learning task category and improve documentation

README.md

@@ -1,96 +1,98 @@
----
-configs:
-- config_name: alfworld
-  data_files:
-  - split: test
-    path: "data/alfworld/**/*.json"
-- config_name: webshop
-  data_files:
-  - split: test
-    path: "data/webshop/**/*.json"
-- config_name: blocksworld
-  data_files:
-  - split: test
-    path: "data/blocksworld/**/*.json"
-- config_name: scienceworld
-  data_files:
-  - split: test
-    path: "data/scienceworld/**/*.json"
-- config_name: textworld
-  data_files:
-  - split: test
-    path: "data/textworld/**/*.json"
-    
-default_config_name: alfworld
----
-
-# 🌟 RewardPrediction: A Fine-grained Step-wise Reward Prediction Benchmark
-
-[**🌐 Website**](https://statefactory.github.io) | [**💻 GitHub**](https://github.com/yijunshens/statefactory) | [**📄 arXiv**](https://arxiv.org/abs/2603.09400)
-
-![Project Teaser](assets/rewardprediction_demo.png)
-
-**RewardPrediction** is a large-scale benchmark designed to evaluate fine-grained, step-wise reward prediction across five diverse text-based environments: **AlfWorld**, **ScienceWorld**, **TextWorld**, **WebShop**, and **BlocksWorld**. It comprises a total of 2,454 unique trajectories with dense reward annotations.
-
-To prevent heuristic reward hacking, we structured the benchmark using a **paired positive-negative strategy**:
-
-* **Positive Trajectories**: Expert demonstrations augmented with random interaction steps at the boundaries.
-* **Negative Trajectories**: Failure trajectories generated via a random policy.
-
----
-
-## 📥 Load RewardPrediction Benchmark
-
-Our benchmark can be loaded from the 🤗 huggingface repo at [YijunShen/RewardPrediction](https://huggingface.co/datasets/YijunShen/RewardPrediction) (reconstructing the `alfworld/`, `webshop/` folders, etc.).
-
-```python
-from huggingface_hub import snapshot_download
-import shutil, os; from pathlib import Path
-
-# [Optional] Your Hugging Face token (e.g., "hf_...") to avoid rate limits
-HF_TOKEN = None 
-
-# 1. Download the raw files from the repository
-snapshot_download(
-    repo_id="YijunShen/RewardPrediction", 
-    repo_type="dataset", 
-    local_dir="rewardprediction",
-    token=HF_TOKEN
-)
-
-# 2. Unwrap 'data' folder to restore the original environment tree
-d = Path("rewardprediction/data")
-if d.exists():
-    [shutil.move(str(i), "rewardprediction") for i in d.iterdir()]
-    d.rmdir()
-
-print(f"✨ Original structure restored at: {os.path.abspath('rewardprediction')}")
-```
-
-## 📄 Data Schema
-
-Each row in the dataset represents a **complete task trajectory**. The data features a nested structure to efficiently store sequential interactions:
-
-* **goal description** (string): The natural language goal the agent needs to achieve for this specific trajectory.
-* **trajectory** (list): A nested sequence of interaction steps. Each step contains the following fields:
-    * **action** (string): The specific action executed by the agent at this time step.
-    * **observation** (string): The textual feedback/observation returned by the environment.
-    * **reward** (dict): A dictionary containing fine-grained reward labels:
-        * `raw` (float): The native, sparse environment reward (usually 1.0 for success, 0.0 otherwise).
-        * `shaped` (float): The interpolated, step-wise ground-truth reward.
-        * `is_expert` (boolean): Indicates whether this step is part of an expert demonstration.
-
-## ✍️ Citation
-
-If you find this dataset helpful for your research, please cite our work:
-```
-@misc{shen2026StateFactory,
-      title={Reward Prediction with Factorized World States}, 
-      author={Yijun Shen and Delong Chen and Xianming Hu and Jiaming Mi and Hongbo Zhao and Kai Zhang and Pascale Fung},
-      year={2026},
-      eprint={2603.09400},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL},
-      url={https://arxiv.org/abs/2603.09400}, 
-}
+---
+configs:
+- config_name: alfworld
+  data_files:
+  - split: test
+    path: data/alfworld/**/*.json
+- config_name: webshop
+  data_files:
+  - split: test
+    path: data/webshop/**/*.json
+- config_name: blocksworld
+  data_files:
+  - split: test
+    path: data/blocksworld/**/*.json
+- config_name: scienceworld
+  data_files:
+  - split: test
+    path: data/scienceworld/**/*.json
+- config_name: textworld
+  data_files:
+  - split: test
+    path: data/textworld/**/*.json
+default_config_name: alfworld
+task_categories:
+- reinforcement-learning
+---
+
+# 🌟 RewardPrediction: A Fine-grained Step-wise Reward Prediction Benchmark
+
+[**🌐 Website**](https://statefactory.github.io) | [**💻 GitHub**](https://github.com/yijunshens/statefactory) | [**📄 Paper**](https://huggingface.co/papers/2603.09400)
+
+**RewardPrediction** is a large-scale benchmark designed to evaluate fine-grained, step-wise reward prediction across five diverse text-based environments: **AlfWorld**, **ScienceWorld**, **TextWorld**, **WebShop**, and **BlocksWorld**. It comprises a total of 2,454 unique trajectories with dense reward annotations.
+
+This dataset was introduced in the paper [Reward Prediction with Factorized World States](https://huggingface.co/papers/2603.09400).
+
+To prevent heuristic reward hacking, the benchmark uses a **paired positive-negative strategy**:
+
+* **Positive Trajectories**: Expert demonstrations augmented with random interaction steps at the boundaries.
+* **Negative Trajectories**: Failure trajectories generated via a random policy.
+
+---
+
+## 📥 Sample Usage
+
+The following code snippet demonstrates how to download the raw files and restore the original environment tree structure as intended by the authors:
+
+```python
+from huggingface_hub import snapshot_download
+import shutil, os; from pathlib import Path
+
+# [Optional] Your Hugging Face token (e.g., "hf_...") to avoid rate limits
+HF_TOKEN = None 
+
+# 1. Download the raw files from the repository
+snapshot_download(
+    repo_id="YijunShen/RewardPrediction", 
+    repo_type="dataset", 
+    local_dir="rewardprediction",
+    token=HF_TOKEN
+)
+
+# 2. Unwrap 'data' folder to restore the original environment tree
+d = Path("rewardprediction/data")
+if d.exists():
+    [shutil.move(str(i), "rewardprediction") for i in d.iterdir()]
+    d.rmdir()
+
+print(f"✨ Original structure restored at: {os.path.abspath('rewardprediction')}")
+```
+
+## 📄 Data Schema
+
+Each row in the dataset represents a **complete task trajectory**. The data features a nested structure to store sequential interactions:
+
+* **goal_description** (string): The natural language goal the agent needs to achieve for this specific trajectory.
+* **trajectory** (list): A nested sequence of interaction steps. Each step contains the following fields:
+    * **action** (string): The specific action executed by the agent at this time step.
+    * **observation** (string): The textual feedback/observation returned by the environment.
+    * **reward** (dict): A dictionary containing fine-grained reward labels:
+        * `raw` (float): The native, sparse environment reward (usually 1.0 for success, 0.0 otherwise).
+        * `shaped` (float): The interpolated, step-wise ground-truth reward.
+        * `is_expert` (boolean): Indicates whether this step is part of an expert demonstration.
+
+## ✍️ Citation
+
+If you find this dataset helpful for your research, please cite:
+
+```bibtex
+@misc{shen2026StateFactory,
+      title={Reward Prediction with Factorized World States}, 
+      author={Yijun Shen and Delong Chen and Xianming Hu and Jiaming Mi and Hongbo Zhao and Kai Zhang and Pascale Fung},
+      year={2026},
+      eprint={2603.09400},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2603.09400}, 
+}
 ```