diff --git a/projects/thinking_midtraining/README.md b/projects/thinking_midtraining/README.md
index 82a41f3..6f62edf 100644
--- a/projects/thinking_midtraining/README.md
+++ b/projects/thinking_midtraining/README.md
@@ -58,7 +58,7 @@ $\tilde{\mathcal{D}} = \{\tilde{c}^1, \tilde{c}^2, \ldots, \tilde{c}^N\}$.
 
 ### 2) Thinking SFT Mid-training
 
-We perform supervised fine-tuning (SFT) mid-training on half of the augmented corpus, which we call $$\tilde{\mathcal{D}}\_{\text{SFT}}$$, using standard next-token prediction. Given a base model $$\mathcal{M}\_{\text{base}}$$ parameterized by $\theta$, we optimize the following objective: $\mathcal{L}\_{\text{SFT}}(\theta) = -\mathbb{E}\_{\tilde{c}^i \sim \tilde{\mathcal{D}}} \left[ \sum_{j=1}^{|\tilde{c}^i|} \log P_\theta(\tilde{c}^i_j \mid \tilde{c}^i_{<j}) \right]$
+We perform supervised fine-tuning (SFT) mid-training on half of the augmented corpus, which we call $\tilde{\mathcal{D}}_{\text{SFT}}$, using standard next-token prediction. Given a base model $\mathcal{M}_{\text{base}}$ parameterized by $\theta$, we optimize the following objective: $\mathcal{L}_{\text{SFT}}(\theta) = -\mathbb{E}_{\tilde{c}^i \sim \tilde{\mathcal{D}}} \left[ \sum_{j=1}^{|\tilde{c}^i|} \log P_\theta(\tilde{c}^i_j \mid \tilde{c}^i_{<j}) \right]$
 
 where $\tilde{c}^i_j$ denotes the $j$-th token in the augmented chunk $\tilde{c}^i$, and $\tilde{c}^i_{<j}$ represents all preceding tokens. Importantly, the loss is computed over the entire augmented sequence, including both the original content tokens $x_j$ and the generated thought tokens $\tau_j$. This allows the model to learn to produce intermediate reasoning steps alongside the original content.
 
@@ -70,17 +70,17 @@ This SFT mid-training phase serves as an intermediate step between initial pretr
 
 While SFT mid-training encourages the model to imitate the teacher's reasoning patterns, it does not directly optimize for the utility of the generated thoughts. To address this, we introduce a reinforcement learning mid-training phase to further refine the model's reasoning capabilities on pretraining data.
 
-Given the second half of the augmented pretraining corpus $\tilde{\mathcal{D}}\_{RL}$, we process each chunk $\tilde{c}^i$ by splitting it into a prefix $p^i$ and a suffix $s^i$:
-$\tilde{c}^i = [p^i, s^i]$ where $p^i$ consists of the initial $l$ tokens and $s^i$ contains the remaining tokens, with $l < |\tilde{c}^i|$. For each prefix $p^i$, the model being mid-trained $\mathcal{M}\_{\text{mid}}$ is tasked with generating a sequence of "thinking" tokens $\hat{\tau}^i$ followed by a predicted suffix $\hat{s}^i$: $[\hat{\tau}^i, \hat{s}^i] = \mathcal{M}\_{\text{mid}}(p^i)$, where $\hat{\tau}^i$ represents the model's intermediate reasoning steps and $\hat{s}^i$ is its prediction of the ground truth suffix $s^i$.
+Given the second half of the augmented pretraining corpus $\tilde{\mathcal{D}}_{RL}$, we process each chunk $\tilde{c}^i$ by splitting it into a prefix $p^i$ and a suffix $s^i$:
+$\tilde{c}^i = [p^i, s^i]$ where $p^i$ consists of the initial $l$ tokens and $s^i$ contains the remaining tokens, with $l < |\tilde{c}^i|$. For each prefix $p^i$, the model being mid-trained $\mathcal{M}_{\text{mid}}$ is tasked with generating a sequence of "thinking" tokens $\hat{\tau}^i$ followed by a predicted suffix $\hat{s}^i$: $[\hat{\tau}^i, \hat{s}^i] = \mathcal{M}_{\text{mid}}(p^i)$, where $\hat{\tau}^i$ represents the model's intermediate reasoning steps and $\hat{s}^i$ is its prediction of the ground truth suffix $s^i$.
 
-To evaluate the quality of the generated suffix, we employ a LLM as a judge. The judge, $\mathcal{M}\_{\text{judge}}$ receives both the generated suffix $\hat{s}^i$ and the ground truth $s^i$, and outputs a binary reward $r^i \in \{0, 1\}$ indicating whether $\hat{s}^i$ matches $s^i$ sufficiently well according to predefined criteria (e.g., semantic similarity, factual correctness, or task completion): $r^i = \mathcal{M}\_{\text{judge}}(\hat{s}^i, s^i)$.
+To evaluate the quality of the generated suffix, we employ a LLM as a judge. The judge, $\mathcal{M}_{\text{judge}}$ receives both the generated suffix $\hat{s}^i$ and the ground truth $s^i$, and outputs a binary reward $r^i \in \{0, 1\}$ indicating whether $\hat{s}^i$ matches $s^i$ sufficiently well according to predefined criteria (e.g., semantic similarity, factual correctness, or task completion): $r^i = \mathcal{M}_{\text{judge}}(\hat{s}^i, s^i)$.
 
 
 
 The RL objective is then to maximize the expected reward over the augmented corpus:
 
 $$
-\mathcal{L}\_{\text{RLVR}}(\theta) = -\mathbb{E}\_{p^i \sim \tilde{\mathcal{D}}} [ \mathbb{E}\_{[\hat{\tau}^i, \hat{s}^i] \sim \mathcal{M}\_{\text{mid}}(\cdot \mid p^i)} [r^i] ]
+\mathcal{L}_{\text{RLVR}}(\theta) = -\mathbb{E}_{p^i \sim \tilde{\mathcal{D}}} [ \mathbb{E}_{[\hat{\tau}^i, \hat{s}^i] \sim \mathcal{M}_{\text{mid}}(\cdot \mid p^i)} [r^i] ]
 $$
 
 where $\theta$ are the parameters of the model. We optimize this objective using DrGRPO.
@@ -90,10 +90,10 @@ By incorporating RL mid-training, our method encourages the model not only to im
 
 ### RL Post-Training
 
-The final stage of the pipeline is to run standard post-training. Given a set of questions $\mathcal{Q}$ from a post-training dataset, the model being post-trained $\mathcal{M}\_{\text{post}}$ generates thoughts $\tau$ and answer $\hat{y}^i$ for each question $Q^i \in \mathcal{Q}$. We employ a rule-based reward model, $\mathcal{M}\_{\text{RLVR}}$ to score the responses compare to the ground truth $y^i$: $r^i = \mathcal{M}\_{\text{RLVR}}(\hat{y}^i, y^i)$.
+The final stage of the pipeline is to run standard post-training. Given a set of questions $\mathcal{Q}$ from a post-training dataset, the model being post-trained $\mathcal{M}_{\text{post}}$ generates thoughts $\tau$ and answer $\hat{y}^i$ for each question $Q^i \in \mathcal{Q}$. We employ a rule-based reward model, $\mathcal{M}_{\text{RLVR}}$ to score the responses compare to the ground truth $y^i$: $r^i = \mathcal{M}_{\text{RLVR}}(\hat{y}^i, y^i)$.
 
 $$
-\mathcal{L}\_{\text{RLVR}}(\theta) = -\mathbb{E}\_{p^i \sim \mathcal{P}} [ \mathbb{E}\_{\hat{y}^i \sim \mathcal{M}\_{\text{post}}(\cdot \mid Q^i)} [r^i] ]
+\mathcal{L}_{\text{RLVR}}(\theta) = -\mathbb{E}_{p^i \sim \mathcal{P}} [ \mathbb{E}_{\hat{y}^i \sim \mathcal{M}_{\text{post}}(\cdot \mid Q^i)} [r^i] ]
 $$
 
 where $\theta$ are the parameters of the model. We optimize this using DrGRPO.