初始化项目，由ModelHub XC社区提供模型

Model: Thrillcrazyer/TACReward7B
Source: Original Platform

README.md

@@ -0,0 +1,48 @@
+---
+
+datasets: DeepMath-103k
+library_name: transformers
+model_name: TACReward7B
+licence: license
+---
+
+<h1 align= "center"> Reasoning-Aware Proxy Reward Model using Process Mining </h1>
+
+<p align="center">
+  <a href="https://pnubaelab.github.io/"><b>BAELAB</b></a>, Pusan National University, Busan, Korea
+</p>
+<p align="center">
+  <a href="https://yongzzai.com/">Yongjae Lee</a><sup>*</sup>, <a href="https://thrillcrazyer.github.io/">Taekyhun Park</a><sup>*</sup> ,  Hyerim Bae<sup>&dagger;</sup>
+</p>
+
+
+
+
+
+
+<p align="center">
+  <a href="https://github.com/Thrillcrazyer/TACReward"><b>🌟 Github</b></a> |
+  <a href="https://huggingface.co/Thrillcrazyer/Qwen-1.5B_THIP"><b>📥 1.5B Download</b></a> |
+  <a href="https://huggingface.co/Thrillcrazyer/TACReward7B"><b>📥 7B Download</b></a> |
+  <a href="https://arxiv.org/abs/2510.25065"><b>📄 Arxiv Paper Link</b></a> |
+</p>
+
+# Abstract
+
+ Recent advances in sparse reward policy gradient methods have enabled effective reinforcement learning (LR) 
+  fine-tuning for post-training language models. However, for reasoning tasks such as mathematical problem solving, 
+  binarized outcome rewards provide limited feedback on intermediate reasoning steps. While some studies have attempted
+  to address this issue by estimating **overall** reasoning quality, it remains unclear whether these rewards are 
+  reliable proxies for the quality of stepwise reasoning. In this study, we consider reasoning as a structured process and 
+  propose **TACReward** reward model. The model can be seamlessly integrated into sparse reward frameworks without 
+  additional human annotation costs or architectural modifications. TACReward aggregates stepwise structural deviations 
+  between teachers and policy reasoning using process mining techniques, producing a scalar output reward range of $[0, 1]$. 
+  Experiments on multiple mathematical reasoning benchmarks demonstrate that integrating the TACReward into sparse reward 
+  frameworks encourages the policy model to improve the structural quality of reasoning. Consequently, this leads to 
+  consistent performance improvements over existing sparse reward frameworks.
+
+# Illustration of TACReward
+
+<div align="center">
+<img src="https://arxiv.org/html/2510.25065v1/x1.png" width="600"/>
+</div>