Research paper proposing self-evolving LLMs trained via data-efficient reinforcement learning by selectively using easy samples. Suggests training efficiency gains by focusing on simpler examples rather than hard negatives.
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Easy Samples Are All You Need: Self-Evolving LLMs via Data-Efficient Reinforcement Learning
Focusing reinforcement learning on easy samples rather than hard negatives significantly improves LLM training data efficiency, challenging conventional wisdom that harder examples drive learning.
Wednesday, April 22, 2026 12:00 PM UTC2 MIN READSOURCE: arXiv CS.LG (Machine Learning)BY sys://pipeline
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