Update README.md

README.md

@@ -24,6 +24,23 @@ Overview
 This translation model was built from the ground up, starting with raw text data and ending with a fully functional bilingual
 English ↔ Russian language model. No pretrained translation models were used at any stage. The project emphasizes data curation, 
 tokenizer design, architectural discipline, and alignment through supervised fine-tuning.
+
+### Training Procedure
+
+The model was trained end-to-end from random initialization using a causal language modeling objective.
+
+- Optimizer: AdamW  
+- Loss: Cross-entropy (next-token prediction)  
+- Training strategy:
+  - Stage 1: Monolingual English + Russian language modeling
+  - Stage 2: Interleaved bilingual language modeling
+  - Stage 3: Bidirectional translation alignment fine-tuning
+- Gradient accumulation used to simulate larger batch sizes
+- Checkpoints saved periodically and manually evaluated
+- Training conducted on consumer-grade GPUs
+
+The focus was on stability, coherence, and alignment rather than maximum scale.
+
 1. Data Collection & Cleaning
 
 The process began with a corpus of approximately 40 public-domain English books, which were:
@@ -130,6 +147,19 @@ and technical texts.
 - No safety fine-tuning
 - Not suitable for production or legal/medical use
 
+### Alignment Perspective
+
+This project explores how structured data curation and constrained supervision can reduce hallucination and improve faithfulness in small-to-mid-scale language models.
+
+Key alignment-relevant aspects:
+- Conditioning without natural-language instructions
+- Strict source-target alignment
+- Avoidance of RLHF or preference modeling
+- Observation of failure modes under ambiguity
+
+The model is intentionally not instruction-tuned to preserve interpretability of its learned representations.
+
+
 ## Reproducibility
 Inference example:
 ```python