LADDER: Self-Improving LLMs Through Recursive Problem Decomposition

• URL: http://arxiv.org/abs/2503.00735v3
• Date: Wed, 05 Mar 2025 11:50:24 GMT
• Title: LADDER: Self-Improving LLMs Through Recursive Problem Decomposition
• Authors: Toby Simonds, Akira Yoshiyama,
• Abstract summary: LADDER is a framework which enables Large Language Models to autonomously improve their problem-solving capabilities.<n>We demonstrate LADDER's effectiveness in the subject of mathematical integration.<n>We also introduce TTRL, where we perform reinforcement learning on variants of test problems at inference time.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce LADDER (Learning through Autonomous Difficulty-Driven Example Recursion), a framework which enables Large Language Models to autonomously improve their problem-solving capabilities through self-guided learning by recursively generating and solving progressively simpler variants of complex problems. Unlike prior approaches that require curated datasets or human feedback, LADDER leverages a model's own capabilities to generate easier question variants. We demonstrate LADDER's effectiveness in the subject of mathematical integration, improving Llama 3.2 3B's accuracy from 1% to 82% on undergraduate-level problems and enabling Qwen2.5 7B Deepseek-R1 Distilled to achieve 73% on the MIT Integration Bee qualifying examination. We also introduce TTRL (Test-Time Reinforcement Learning), where we perform reinforcement learning on variants of test problems at inference time. TTRL enables Qwen2.5 7B Deepseek-R1 Distilled to achieve a state-of-the-art score of 90% on the MIT Integration Bee qualifying examination, surpassing OpenAI o1's performance. These results show how self-directed strategic learning can achieve significant capability improvements without relying on architectural scaling or human supervision.

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