VPR-Qwen3-4B-Base-Math-Mixed
This checkpoint is trained from Qwen3-4B-Base with mixed math and VPR game experience across Sokoban, Sudoku, and Minesweeper. VPR supplies action-level process rewards through task-grounded oracles and state-group rollout, while math training follows the mixed-training protocol described in the paper.
Reported results
| Evaluation | Metric | Result |
|---|---|---|
| General-reasoning OOD suite | Macro average | 52.75 |
| ALFWorld | SR | 16.12 ± 2.87 |
| WebShop | Score | 42.01 ± 2.14 |
| WebShop | SR | 1.33 ± 0.76 |
Values are reported under the VPR paper's evaluation protocol. SR is success rate. The general-reasoning value is the macro average across the reported OOD benchmarks. These OOD results are not a claim of exactly matched total rollout compute across training methods.
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "nics-efc/VPR-Qwen3-4B-Base-Math-Mixed"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
)
inputs = tokenizer("Solve the task step by step.", return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=1024)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Use the VPR codebase for the exact prompts, environments, and evaluation entry points.
Resources
Limitations
The checkpoint is shaped by the documented math distribution, task-grounded game oracles, prompts, and action formats. Performance and safety outside those settings have not been established.
Citation
@misc{yuan2026verifiable,
title = {Verifiable Process Rewards for Agentic Reasoning},
author = {Huining Yuan and Zelai Xu and Huaijie Wang and Xiangmin Yi and Jiaxuan Gao and Xiao-Ping Zhang and Yu Wang and Chao Yu and Yi Wu},
year = {2026},
eprint = {2605.10325},
archivePrefix = {arXiv},
primaryClass = {cs.AI},
url = {https://arxiv.org/abs/2605.10325}
}