# Distilling Algorithmic Reasoning from LLMs via Explaining Solution Programs

arXiv: https://arxiv.org/abs/2404.08148 · HTML: https://mlsanity.com/paper/2404.08148/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 3 of its 30 references that are in the map.
- First posted on arXiv 11 Apr 2024.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

## Builds on (most cited first, 3 of 3)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Neural Program Search: Solving Programming Tasks from Description and Examples](https://mlsanity.com/paper/1802.04335/) (2018)

## Built on by (most cited first, 3 of 3)

- [Antidistillation Sampling](https://mlsanity.com/paper/2504.13146/) (2025)
- [AutoSDT: Scaling Data-Driven Discovery Tasks Toward Open Co-Scientists](https://mlsanity.com/paper/2506.08140/) (2025)
- [Aggressive Compression Enables LLM Weight Theft](https://mlsanity.com/paper/2601.01296/) (2026)

## Abstract (arXiv)

Distilling explicit chain-of-thought reasoning paths has emerged as an effective method for improving the reasoning abilities of large language models (LLMs) across various tasks. However, when tackling complex tasks that pose significant challenges for state-of-the-art models, this technique often struggles to produce effective chains of thought that lead to correct answers. In this work, we propose a novel approach to distill reasoning abilities from LLMs by leveraging their capacity to explain solutions. We apply our method to solving competitive-level programming challenges. More specifically, we employ an LLM to generate explanations for a set of <problem, solution-program> pairs, then use <problem, explanation> pairs to fine-tune a smaller language model, which we refer to as the Reasoner, to learn algorithmic reasoning that can generate "how-to-solve" hints for unseen problems. Our experiments demonstrate that learning from explanations enables the Reasoner to more effectively guide program implementation by a Coder, resulting in higher solve rates than strong chain-of-thought baselines on competitive-level programming problems. It also outperforms models that learn directly from <problem, solution-program> pairs. We curated an additional test set in the CodeContests format, which includes 246 more recent problems posted after the models' knowledge cutoff.