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Atomic Skills are the Prerequisite: When Reinforcement Learning Synthesizes Compositional Reasoning, and When It Only Amplifies

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RL bridges this generalization gap, acting as a skill synthesizer rather than a mere amplifier--but only under a strict prerequisite: it synthesizes new composite strategies only when the base model has first mastered the independent atomic skills via SFT.

Outcome-reward reinforcement learning enables language models to compose learned atomic skills into new skills beyond next-token training on the same data.Supported

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Link: https://arxiv.org/abs/2512.01970
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