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README.md

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+---
+license: cc-by-nc-4.0
+library_name: transformers
+pipeline_tag: text-classification
+base_model: meta-llama/Meta-Llama-3-8B
+---
+
+# eng_prm
+
+This model is a Process Reward Model (PRM) trained to evaluate **engagement** in mathematical reasoning tasks. It was introduced as part of the **MAHALO** (Multi-Action-Head ALignment with PRM-guided DecOding) framework in the paper [Simultaneous Multi-objective Alignment Across Verifiable and Non-verifiable Rewards](https://huggingface.co/papers/2510.01167).
+
+The model is based on `Meta-Llama-3-8B` and fine-tuned as a sequence classifier to provide step-level reward signals.
+
+## Resources
+
+- **Paper:** [Simultaneous Multi-objective Alignment Across Verifiable and Non-verifiable Rewards](https://huggingface.co/papers/2510.01167)
+- **GitHub Repository:** [pearls-lab/multiobj-align](https://github.com/pearls-lab/multiobj-align)
+
+## Model Description
+
+MAHALO is a unified framework that standardizes PRM training across verifiable and non-verifiable settings for step-level supervision. This specific checkpoint serves as the Engagement PRM for the math domain, enabling controllable inference through objective-specific weighting and PRM-guided decoding.
+
+## Citation
+
+If you find this work useful, please consider citing:
+
+```bibtex
+@article{shen2025simultaneous,
+  title={Simultaneous Multi-objective Alignment Across Verifiable and Non-verifiable Rewards},
+  author={Shen, Yiran and Xia, Yu and Chang, Jonathan and Ammanabrolu, Prithviraj},
+  journal={arXiv preprint arXiv:2510.01167},
+  year={2025},
+  url={https://arxiv.org/abs/2510.01167}
+}
+```