AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work studies cross-lingual mathematical reasoning transfer in English-centric LLMs under controlled multilingual evaluation and finds that transfer is highly variable across initial model choice, target language, and training paradigm.
This study systematically explores the merging behavior of RL-trained LLMs compared to those trained with traditional SFT, finding that RL significantly reduces task conflicts and results in less performance degradation after merging, making RL-trained models particularly well-suited for this process.
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