Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+language:
+- en
+metrics:
+- accuracy
+base_model:
+- Qwen/Qwen2.5-1.5B-Instruct
+pipeline_tag: reinforcement-learning
+tags:
+- agent
+- reinforcement-learning
+- long-horizon
+- embodied-ai
+- strategic-exploration
+---
+
+# SPARK: Strategic Policy-Aware Exploration via Dynamic Branching
+
+This model is trained using the **SPARK** framework proposed in the paper:
+
+**[SPARK: Strategic Policy-Aware Exploration via Dynamic Branching for Long-Horizon Agentic Learning](https://huggingface.co/papers/2601.20209)**
+
+📄 **Paper:** [arXiv:2601.20209](https://arxiv.org/abs/2601.20209)
+
+## Overview
+
+SPARK is a novel reinforcement learning framework that enables autonomous strategic exploration for long-horizon agentic tasks. Instead of uniformly exploring all steps, SPARK selectively branches at critical decision points using intrinsic `<explore>` signals, achieving superior performance with significantly fewer training samples.
+
+## Key Features
+
+- 🎯 **Autonomous Strategic Exploration**: Dynamically identifies critical states for branching without human priors
+- ⚡ **Sample Efficient**: Achieves 84.4% success with only 20% training data (vs. GRPO 76.6% at 100%)
+- 💰 **Token Efficient**: Reduces token consumption by up to 47% through prefix sharing
+- 🌍 **Strong Generalization**: Maintains 80.5% success on unseen tasks (significantly outperforms GRPO)
+
+## Performance Highlights
+
+| Benchmark | SPARK-1.5B | GPT-5 | Gemini-2.5-Pro |
+|-----------|------------|-------|----------------|
+| ALFWorld L2 | **80.5%** | 63.3% | 55.5% |
+| ScienceWorld L2 | **49.2%** | 33.6% | 30.5% |
+| WebShop | **75.8%** | 29.7% | 32.0% |
+
+## Citation
+
+If you use this model or the SPARK framework in your research, please cite:
+```bibtex
+@article{wu2026spark,
+  title={SPARK: Strategic Policy-Aware Exploration via Dynamic Branching for Long-Horizon Agentic Learning},
+  author={Wu, Jinyang and Yang, Shuo and Yang, Changpeng and Shen, Yuhao and Zhang, Shuai and Wen, Zhengqi and Tao, Jianhua},
+  journal={arXiv preprint arXiv:2601.20209},
+  year={2026}
+}
+```
+
+## Model Details
+
+- **Base Model:** Qwen/Qwen2.5-1.5B-Instruct
+- **Training Method:** SPARK (Dynamic Branching RL)
+- **Training Dataset:** WebShop
+
+## Links
+
+- 📄 Paper: https://arxiv.org/abs/2601.20209
+- 🤗 Paper Page: https://huggingface.co/papers/2601.20209