---
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% |

## Quickstart
Here we provide a transformers inference style:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Jinyang23/Spark-1.5B-ScienceWorld"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "Calculate the sum of 123 and 456. Provide only the numerical answer."

messages = [
    {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=512
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)
```

## 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:** ScienceWorld

## Links

- 📄 Paper: https://arxiv.org/abs/2601.20209
- 🤗 Paper Page: https://huggingface.co/papers/2601.20209