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Arxiv
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From Data-Centric to Sample-Centric: Enhancing LLM Reasoning via Progressive Optimization
- Researchers introduce LPPO framework to enhance Large Language Models' reasoning capabilities through progressive optimization.
- LPPO framework leverages a small set of high-quality demonstrations using prefix-guided sampling and learning-progress weighting.
- Prefix-guided sampling augments data with partial solution prefixes from expert demonstrations to improve policy guidance.
- Learning-progress weighting adjusts sample influence based on model progression, leading to faster convergence and improved performance on mathematical-reasoning benchmarks.
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