VPR-Qwen3-4B-Sudoku
This is a Qwen3-4B checkpoint trained with Verifiable Process Rewards (VPR) on Markovian Sudoku interactions. At each visited state, VPR samples four action responses, scores their parsed actions with a task-grounded constraint-based Sudoku oracle, commits one highest-reward candidate, and optimizes all eligible candidates using locally normalized advantages.
Reported result
| Evaluation | SR | CR |
|---|---|---|
| Sudoku | 80.60 ± 4.16 | 84.22 ± 3.15 |
Values are percentages reported under the evaluation protocol in the VPR paper. SR is success rate; CR is completion rate.
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "nics-efc/VPR-Qwen3-4B-Sudoku"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
)
inputs = tokenizer("<current Markovian game prompt>", return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Use the environment prompts, parsers, and action conventions in the VPR codebase for reproduction. These task-specific checkpoints are not intended as general-purpose assistants.
Resources
Limitations
Training relies on task-grounded oracle signals and specific Markovian prompts. Performance outside the documented environments and action formats has not been established. Evaluate safety and correctness before open-ended deployment.
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}
}