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-# Babysitting a Small Language Model through One-Step Tree-of-Thoughts Knowledge Distillation
+# Babysitting a Small Language Model through One-Step Tree-of-Thoughts Knowledge Distillation
+
+## Overview
+Paper: https://www.zichenz.me/project/slm_tot/slm_tot.pdf
+
+This repository contains the code and datasets used for the paper **"Babysitting a Small Language Model through One-Step Tree-of-Thoughts Knowledge Distillation"**. The project explores a novel approach to enhance the reasoning capabilities of Small Language Models (SLMs) using a simplified prompting method called One-Step Tree-of-Thoughts (ToT) and knowledge distillation from Large Language Models (LLMs).
+
+## Methods and Results
+
+The project addresses the limitations of SLMs in handling complex reasoning tasks by:
+- Introducing the **One-Step Tree-of-Thoughts** prompting framework.
+- Fine-tuning SLMs using a synthesized dataset derived from LLM-generated responses.
+- Evaluating the performance on the **Game of 24** reasoning benchmark.
+
+Key results:
+- One-Step ToT significantly improves reasoning performance over Chain-of-Thought (CoT) prompting.
+- The fine-tuned SLM achieves competitive accuracy with vastly more efficient resource utilization compared to LLMs.