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+## 🔬 Key Contributions
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+- Demonstrates that domain-balanced continued pretraining on curated multi-domain data (education, math, code, science) yields consistent improvements across commonsense reasoning benchmarks in 1B-scale models
+- Suggests that multi-step mathematical reasoning remains a fundamental bottleneck for 1B-scale models, even when combining math-focused pretraining (OpenWebMath) with instruction tuning (MetaMathQA)
+- Provides a fully reproducible, compute-efficient training recipe (CPT → LoRA SFT) built and executed **by a single undergraduate student in under one week**, demonstrating that meaningful LLM research is achievable without institutional resources or large teams
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 ## 📊 Benchmark Results
 
 All scores measured with [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) under **identical conditions** (same prompts, same few-shot settings, same hardware).