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Computer Science > Machine Learning

arXiv:2503.00735 (cs)
[Submitted on 2 Mar 2025 (v1), last revised 5 Mar 2025 (this version,
v3)]

Title:LADDER: Self-Improving LLMs Through Recursive Problem
Decomposition

Authors:Toby Simonds, Akira Yoshiyama
View a PDF of the paper titled LADDER: Self-Improving LLMs Through
Recursive Problem Decomposition, by Toby Simonds and 1 other authors
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    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.

Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as:  arXiv:2503.00735 [cs.LG]
          (or arXiv:2503.00735v3 [cs.LG] for this version)
          https://doi.org/10.48550/arXiv.2503.00735
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Akira Yoshiyama [view email]
[v1] Sun, 2 Mar 2025 05:16:43 UTC (286 KB)
[v2] Tue, 4 Mar 2025 14:30:32 UTC (203 KB)
[v3] Wed, 5 Mar 2025 11:50:24 UTC (203 KB)
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