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nics-efc
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Model Details
Model Provider
nics-efc
Model Tree
Qwen/Qwen3-4B-Base
Input Modalities
Output Modalities
Supported Functionality
| 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.
python
from transformers import AutoModelForCausalLM, AutoTokenizermodel_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.
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.
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}}