---
license: apache-2.0
base_model: Qwen/Qwen2.5-7B
library_name: transformers
pipeline_tag: text-generation
language:
- en
datasets:
- XINLI1997/WirelessMATHBench-XL
tags:
- wireless-communications
- mathematical-reasoning
- grpo
- reinforcement-learning
- neurips-2026
---

# WirelessMathLM-7B

GRPO-trained reference checkpoint from **WirelessMathBench-XL: An Auditable Benchmark for Wireless Mathematical Reasoning** (NeurIPS 2026, Evaluations and Datasets Track).

[Project page](https://lixin.ai/WirelessMathBench-XL/) · [arXiv](https://arxiv.org/abs/2509.23219) · [OpenReview](https://openreview.net/forum?id=KbbNUVldqT) · [Code](https://github.com/LiXin97/WirelessMathBench-XL) · [Dataset](https://huggingface.co/datasets/XINLI1997/WirelessMATHBench-XL)

## Model

- **Base:** [Qwen/Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2.5-7B). Initialised from the **Qwen2.5-7B** base checkpoint (no SFT warm-start).
- **Training:** GRPO with [EasyR1](https://github.com/hiyouga/EasyR1) on the WirelessMathBench-XL train split (3,227 problems), 40 epochs / 240 steps, composite reward 0.1 × format + 0.9 × verifier accuracy, AdamW (lr 1e-6, cosine), KL coefficient 0.01, 4 × NVIDIA A6000. See Appendix B of the paper.
- **Precision:** bfloat16.

## Results

| Evaluation (WirelessMathBench-XL test) | Accuracy |
|---|---:|
| Full 800-item test split (paper Tab. 3, row D10) | 47.88% |
| 310-item public test subset (paper Appendix L) | 46.45% |

Locked protocol: 2k-token answer budget, T = 0.6, hierarchical verifier (deterministic match, canonicalisation, GPT-4.1-mini fallback judge).

Training-time check under greedy model selection (paper Tab. 7): base 21.88% → GRPO 39.50%. These values use a different protocol from the locked scores.

## Usage

The paper queries these checkpoints through a raw completion endpoint (no chat template), using the dataset's `prompt` field.

```python
from datasets import load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "XINLI1997/WirelessMathLM-7B"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo, torch_dtype="auto", device_map="auto")

ex = load_dataset("XINLI1997/WirelessMATHBench-XL", "cc_by", split="test")[0]
prompt = ex["prompt"]
inputs = tok(prompt, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=2048, do_sample=True, temperature=0.6)
print(tok.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
```

## Limitations

- These are **release-split learnability checks**, not evidence of general or verifier-independent reasoning. The train/test split is problem-level, so train and test share source papers.
- The reward verifier and the evaluation fallback share the same checkpoint family and prompt style.
- Intended for research on wireless mathematical reasoning; not a general-purpose assistant.

## License

Apache 2.0, following the base model.

## Citation

```bibtex
@inproceedings{
li2026wirelessmathbenchxl,
title={WirelessMathBench-XL: An Auditable Benchmark for Wireless Mathematical Reasoning},
author={Xin Li and Mengbing Liu and Yiyang Zhu and Wenhe Zhang and Li Wei and Jiancheng An and Chau Yuen},
booktitle={The Fortieth Annual Conference on Neural Information Processing Systems Evaluations and Datasets Track},
year={2026}
}
```