Improve dataset card: Add paper link, task categories, tags, abstract, sample usage, and citation

This pull request significantly enhances the `RouteFinder` dataset card by:
- Adding `graph-ml` to `task_categories` and `vrp`, `vehicle-routing-problems` to `tags` for improved discoverability.
- Including a direct link to the paper: [RouteFinder: Towards Foundation Models for Vehicle Routing Problems](https://huggingface.co/papers/2406.15007).
- Adding a detailed introduction based on the paper's abstract.
- Providing a "Sample Usage" section with bash commands from the official GitHub repository for downloading data and running experiments.
- Including the comprehensive table of 48 supported Vehicle Routing Problem variants.
- Adding the official BibTeX citation for the paper.

These updates make the dataset card much more informative and user-friendly for the community.

README.md

@@ -1,15 +1,113 @@
 ---
 license: mit
+size_categories:
+- 10K<n<100K
 task_categories:
 - reinforcement-learning
+- graph-ml
 tags:
 - NCO
 - AI4CO
 - RL4CO
-size_categories:
-- 10K<n<100K
+- vrp
+- vehicle-routing-problems
 ---
 
 # RouteFinder Data
 
-You may find instructions here: https://github.com/ai4co/routefinder
+This repository contains datasets used and generated by **RouteFinder**, a comprehensive foundation model framework to tackle various Vehicle Routing Problem (VRP) variants.
+
+**Paper**: [RouteFinder: Towards Foundation Models for Vehicle Routing Problems](https://huggingface.co/papers/2406.15007)
+**GitHub Repository**: [https://github.com/ai4co/routefinder](https://github.com/ai4co/routefinder)
+
+## Abstract
+This paper introduces RouteFinder, a comprehensive foundation model framework to tackle different Vehicle Routing Problem (VRP) variants. Our core idea is that a foundation model for VRPs should be able to represent variants by treating each as a subset of a generalized problem equipped with different attributes. We propose a unified VRP environment capable of efficiently handling any combination of these attributes. The RouteFinder model leverages a modern transformer-based encoder and global attribute embeddings to improve task representation. Additionally, we introduce two reinforcement learning techniques to enhance multi-task performance: mixed batch training, which enables training on different variants at once, and multi-variant reward normalization to balance different reward scales. Finally, we propose efficient adapter layers that enable fine-tuning for new variants with unseen attributes. Extensive experiments on 48 VRP variants show RouteFinder outperforms recent state-of-the-art learning methods.
+
+## Sample Usage
+
+To get started with RouteFinder, you can download the data and checkpoints, and then run an experiment using the provided scripts.
+
+### Download data and checkpoints
+To download the data and checkpoints from Hugging Face automatically, you can use:
+
+```bash
+python scripts/download_hf.py
+```
+
+### Running an experiment
+You can run a main experiment, for example, with the RouteFinder Transformer model:
+
+```bash
+python run.py experiment=main/rf/rf-transformer-100
+```
+For more detailed instructions, configuration options, and other scripts (e.g., for testing or data generation), please refer to the [GitHub repository](https://github.com/ai4co/routefinder).
+
+## Available Environments
+
+The dataset provides instances for 48 VRP variants. All variants include the base Capacity (C). The five features Open Route (O), Backhaul (B), Duration Limit (L), Time Windows (TW), and Multi-depot (MD) can be combined into any subset, creating $2^5 = 32$ possible combinations. The Mixed (M) global feature adds 16 more Mixed Backhaul (MB) variants, resulting in a total of 48 variants.
+
+| **VRP Variant** | **Capacity (C)** | **Open Route (O)** | **Backhaul (B)** | **Mixed (M)** | **Duration Limit (L)** | **Time Windows (TW)** | **Multi-depot (MD)** |
+|:----------------|:----------------:|:------------------:|:----------------:|:-------------:|:----------------------:|:---------------------:|:-------------------:|
+| CVRP | ✔ | | | | | | |
+| OVRP | ✔ | ✔ | | | | | |
+| VRPB | ✔ | | ✔ | | | | |
+| VRPL | ✔ | | | | ✔ | | |
+| VRPTW | ✔ | | | | | ✔ | |
+| OVRPTW | ✔ | ✔ | | | | ✔ | |
+| OVRPB | ✔ | ✔ | ✔ | | | | |
+| OVRPL | ✔ | ✔ | | | ✔ | | |
+| VRPBL | ✔ | | ✔ | | ✔ | | |
+| VRPBTW | ✔ | | ✔ | | | ✔ | |
+| VRPLTW | ✔ | | | | ✔ | ✔ | |
+| OVRPBL | ✔ | ✔ | ✔ | | ✔ | | |
+| OVRPBTW | ✔ | ✔ | ✔ | | | ✔ | |
+| OVRPLTW | ✔ | ✔ | | | ✔ | ✔ | |
+| VRPBLTW | ✔ | | ✔ | | ✔ | ✔ | |
+| OVRPBLTW | ✔ | ✔ | ✔ | | ✔ | ✔ | |
+| VRPMB | ✔ | | ✔ | ✔ | | | |
+| OVRPMB | ✔ | ✔ | ✔ | ✔ | | | |
+| VRPMBL | ✔ | | ✔ | ✔ | ✔ | | |
+| VRPMBTW | ✔ | | ✔ | ✔ | | ✔ | |
+| OVRPMBL | ✔ | ✔ | ✔ | ✔ | ✔ | | |
+| OVRPMBTW | ✔ | ✔ | ✔ | ✔ | | ✔ | |
+| VRPMBLTW | ✔ | | ✔ | ✔ | ✔ | ✔ | |
+| OVRPMBLTW | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | |
+| MDCVRP | ✔ | | | | | | ✔ |
+| MDOVRP | ✔ | ✔ | | | | | ✔ |
+| MDVRPB | ✔ | | ✔ | | | | ✔ |
+| MDVRPL | ✔ | | | | ✔ | | ✔ |
+| MDVRPTW | ✔ | | | | | ✔ | ✔ |
+| MDOVRPTW | ✔ | ✔ | | | | ✔ | ✔ |
+| MDOVRPB | ✔ | ✔ | ✔ | | | | ✔ |
+| MDOVRPL | ✔ | ✔ | | | ✔ | | ✔ |
+| MDVRPBL | ✔ | | ✔ | | ✔ | | ✔ |
+| MDVRPBTW | ✔ | | ✔ | | | ✔ | ✔ |
+| MDVRPLTW | ✔ | | | | ✔ | ✔ | ✔ |
+| MDOVRPBL | ✔ | ✔ | ✔ | | ✔ | | ✔ |
+| MDOVRPBTW | ✔ | ✔ | ✔ | | | ✔ | ✔ |
+| MDOVRPLTW | ✔ | ✔ | | | ✔ | ✔ | ✔ |
+| MDVRPBLTW | ✔ | | ✔ | | ✔ | ✔ | ✔ |
+| MDOVRPBLTW | ✔ | ✔ | ✔ | | ✔ | ✔ | ✔ |
+| MDVRPMB | ✔ | | ✔ | ✔ | | | ✔ |
+| MDOVRPMB | ✔ | ✔ | ✔ | ✔ | | | ✔ |
+| MDVRPMBL | ✔ | | ✔ | ✔ | ✔ | | ✔ |
+| MDVRPMBTW | ✔ | | ✔ | ✔ | | ✔ | ✔ |
+| MDOVRPMBL | ✔ | ✔ | ✔ | ✔ | ✔ | | ✔ |
+| MDOVRPMBTW | ✔ | ✔ | ✔ | ✔ | | ✔ | ✔ |
+| MDVRPMBLTW | ✔ | | ✔ | ✔ | ✔ | ✔ | ✔ |
+| MDOVRPMBLTW | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ |
+
+## Citation
+If you find RouteFinder valuable for your research or applied projects, please cite using the following BibTeX:
+
+```bibtex
+@article{
+berto2025routefinder,
+title={{RouteFinder: Towards Foundation Models for Vehicle Routing Problems}},
+author={Federico Berto and Chuanbo Hua and Nayeli Gast Zepeda and Andr{\'e} Hottung and Niels Wouda and Leon Lan and Junyoung Park and Kevin Tierney and Jinkyoo Park},
+journal={Transactions on Machine Learning Research},
+issn={2835-8856},
+year={2025},
+url={https://openreview.net/forum?id=QzGLoaOPiY},
+}
+```