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nics-efc
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Model Details
Model Provider
nics-efc
Model Tree
Qwen/Qwen3-4B
Input Modalities
Output Modalities
Supported Functionality
| Evaluation | SR | CR |
|---|---|---|
| Minesweeper | 32.60 ± 5.03 | 85.76 ± 0.98 |
Values are percentages reported under the evaluation protocol in the VPR paper. SR is success rate; CR is completion rate.
python
from transformers import AutoModelForCausalLM, AutoTokenizermodel_id = "nics-efc/VPR-Qwen3-4B-Minesweeper"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.
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.
bibtex
@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}}