Update README.md

README.md

@@ -165,14 +165,21 @@ $$ -\frac{1}{\log n}\sum_{j=1}^n p_j \log(p_j+\delta) $$
 
 where \\( (p_j)_{1\leq j\leq n} \\) are the confidence scores, \\( n \\) is the number of confidence scores, \\( \log n \\) is the maximum possible entropy and \\( \delta = 10^{-8} \\) is a small regularization constant. This penalizes overly skewed distributions (e.g., `0.9`/`0.1` splits) in favor of more balanced confidence allocations when multiple interpretations are plausible. Additional penalties were applied for structural issues (missing attributes) and for outputs that attempted to answer queries rather than analyze them.
 
+The training code and the reward function code can be found <ins>here</ins> and <ins>here</ins>, respectively.
+
 ### Phase 2: Breadth-Focused Supervised Fine-Tuning
+
 Following structural learning through GRPO, the model underwent supervised fine-tuning on the complete [krogoldAI/rag-query-analysis](https://huggingface.co/datasets/krogoldAI/rag-query-analysis) dataset (7,305 examples) for 2 epochs with a learning rate of 2e-5. This phase captured broad semantic patterns across diverse query types and domains.
 
+The training code can be found <ins>here</ins>.
+
 ### Phase 3: Depth-Focused Supervised Fine-Tuning
+
 The final phase focused on precision refinement using only the highest-quality 3,660 examples from [krogoldAI/rag-query-analysis](https://huggingface.co/datasets/krogoldAI/rag-query-analysis), i.e., those examples where the judge LLM assigned perfect scores. This "polish" phase was trained for 3 epochs with a learning rate of 2e-5 to maximize learning from gold-standard examples.
 
 This progressive training approach ensures the model achieves both reliable formatting and nuanced semantic understanding. All training was performed on an NVIDIA A100 SXM GPU.
 
+The training code can be found <ins>here</ins>.
 
 ## Model Capabilities