Update README.md

README.md

@@ -12,7 +12,7 @@ licence: license
   <a href="https://pnubaelab.github.io/"><b>BAELAB</b></a>, Pusan National University, Busan, Korea
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-  Taekyhun Park<sup>*</sup> , Yongjae Lee<sup>*</sup>, Hyerim Bae<sup>&dagger;</sup>
+  Yongjae Lee<sup>*</sup>, Taekyhun Park<sup>*</sup> ,  Hyerim Bae<sup>&dagger;</sup>
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@@ -32,9 +32,9 @@ licence: license
  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 \textbf{overall} reasoning quality, it remains unclear whether these rewards are 
+  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 \textbf{TACReward} reward model. The model can be seamlessly integrated into sparse reward frameworks without 
+  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