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From Reasoning Traces to Reusable Modules: Understanding Compositional Generalization in Language Model Reasoning

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Our results demonstrate that RL can extract atomic modules from compound traces supplied by SFT and recombine them to solve new configurations.

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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https://arxiv.org/abs/2606.18089
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  1. Oct 30, 2026Entered the graphEntered the graph; no copy stored