Update README.md

README.md

@@ -39,16 +39,16 @@ These models are trained to develop foundational temporal understanding.
    * *Focus: Foundational logic on easy timestamp inference tasks.*
 * **[Time-R1-S1P2](https://huggingface.co/ulab-ai/Time-R1-S1P2):** Checkpoint after Phase 2 of Stage 1 training.
    * *Focus: Full task exploration on all Stage 1 subtasks with mixed difficulty.*
-* **[Time-R1-Theta1](https://huggingface.co/ulab-ai/Time-R1-Theta1):** Checkpoint $\theta_1$, after Phase 3 (full Stage 1 training).
+* **[Time-R1-Theta1](https://huggingface.co/ulab-ai/Time-R1-Theta1):** Checkpoint θ₁, after Phase 3 (full Stage 1 training).
    * *Focus: Refined precision on all Stage 1 subtasks under stricter evaluation.*
-* **[Time-R1-Theta1_prime](https://huggingface.co/ulab-ai/Time-R1-Theta1_prime):** Ablation model $\theta_1'$, trained for Stage 1 without the dynamic reward design.
+* **[Time-R1-Theta1_prime](https://huggingface.co/ulab-ai/Time-R1-Theta1_prime):** Ablation model θ₁', trained for Stage 1 without the dynamic reward design.
    * *Focus: Serves as a baseline to evaluate the efficacy of the dynamic reward curriculum.*
 
 ### Stage 2: Future Event Time Prediction Model
 
 This model builds upon Stage 1 capabilities to predict future event timings.
 
-* **[Time-R1-Theta2](https://huggingface.co/ulab-ai/Time-R1-Theta2):** Checkpoint $\theta_2$, after Stage 2 training.
+* **[Time-R1-Theta2](https://huggingface.co/ulab-ai/Time-R1-Theta2):** Checkpoint θ₂, after Stage 2 training.
    * *Focus: Predicting the timing of future events occurring after its initial knowledge cutoff.*
 
 Please refer to the [main paper](https://arxiv.org/abs/2505.13508) for detailed discussions on the architecture, training methodology, and comprehensive evaluations.