update README.md

README.md

@@ -36,13 +36,15 @@ _Evaluated on 12 diverse benchmarks covering knowledge, reasoning, coding, and m
 ### Technical Overview
 The training objective combines two complementary losses:
 
-$ \mathcal{L}(\theta) = \mathcal{L}_{\text{SFT}}(\theta) + \lambda \mathcal{L}_{\text{conf}}(\theta) $
+```math
+L(θ) = L_SFT(θ) + λL_conf(θ)
+```
 
 Where:
 
-+ $ \mathcal{L}_{\text{SFT}} $: Supervised fine-tuning loss ensuring prediction correctness
-+ $ \mathcal{L}_{\text{conf}} $: Confidence loss that minimizes entropy only for correctly predicted tokens
-+ $ \lambda $: Hyperparameter balancing the two objectives
++ **L_SFT**: Supervised fine-tuning loss ensuring prediction correctness
++ **L_conf**: Confidence loss that minimizes entropy only for correctly predicted tokens
++ **λ**: Hyperparameter balancing the two objectives
 
 ### Why CAP Works
 1. **Sharpens Correct Predictions**: While standard training ensures correctness, it provides diminishing incentive to increase confidence on already-correct tokens. CAP explicitly optimizes for high-confidence predictions.