Self-Improving Language Models for Evolutionary Program Synthesis: A Case Study on ARC-AGI

• URL: http://arxiv.org/abs/2507.14172v1
• Date: Thu, 10 Jul 2025 15:42:03 GMT
• Title: Self-Improving Language Models for Evolutionary Program Synthesis: A Case Study on ARC-AGI
• Authors: Julien Pourcel, Cédric Colas, Pierre-Yves Oudeyer,
• Abstract summary: Many program synthesis tasks prove too challenging for even state-of-the-art language models to solve in single attempts.<n>We propose SOAR, a method that learns program synthesis by integrating language models into a self-improving evolutionary loop.
• Score: 22.148355836548365
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Many program synthesis tasks prove too challenging for even state-of-the-art language models to solve in single attempts. Search-based evolutionary methods offer a promising alternative by exploring solution spaces iteratively, but their effectiveness remain limited by the fixed capabilities of the underlying generative model. We propose SOAR, a method that learns program synthesis by integrating language models into a self-improving evolutionary loop. SOAR alternates between (1) an evolutionary search that uses an LLM to sample and refine candidate solutions, and (2) a hindsight learning phase that converts search attempts into valid problem-solution pairs used to fine-tune the LLM's sampling and refinement capabilities\, -- \,enabling increasingly effective search in subsequent iterations. On the challenging ARC-AGI benchmark, SOAR achieves significant performance gains across model scales and iterations, leveraging positive transfer between the sampling and refinement finetuning tasks. These improvements carry over to test-time adaptation, enabling SOAR to solve 52\% of the public test set. Our code is open-sourced at: https://github.com/flowersteam/SOAR

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