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This work systematically compares supervised fine-tuning (SFT) and reinforcement learning (RL), evaluating each trained model's self-assessment against its updated capability and measuring how well it retains its solving ability.
A system-level perspective on self-improving language models is presented and a unified framework that organizes existing techniques is introduced that organizes existing techniques and outlines the vision for future research toward fully self-improving LLMs.
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