LADDER: Self-Improving LLMs Through Recursive Problem Decomposition
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.
67c7dc0c9ad085d8f8ffc0cd
Toby Simonds
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2025-03-05T10:11:10.920Z
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Toby Simonds
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TamasSimonds
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Akira Yoshiyama
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2025-03-05T10:11:12.944Z
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Akira Yoshiyama
user
akiray1
LADDER: Self-Improving LLMs Through Recursive Problem Decomposition
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.
LADDER, a framework for autonomous difficulty-driven problem-solving, and TTRL, a test-time reinforcement learning approach, enhance Large Language Models' performance on mathematical integration tasks without human supervision.
LADDER Learning through Autonomous Difficulty-Driven Example Recursion Large Language Models self-guided learning recursive problem-solving Llama 3.2 3B Qwen2.5 7B Deepseek-R1 Distilled mathematical integration TTRL Test-Time Reinforcement Learning MIT Integration Bee qualifying examination