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README.md
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## Enhanced Reasoning: Joint Training with SFT + RLVR + RLHF
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Built upon Ling-mini-2.0-base, Ring-mini-2.0 undergoes further training with Long-CoT SFT, more stable and continuous RLVR, and RLHF joint optimization, significantly improving the stability and generalization of complex reasoning. On multiple challenging benchmarks (LiveCodeBench, AIME 2025, GPQA, ARC-AGI-v1, etc.), it outperforms dense models below 10B and even rivals larger MoE models (e.g., gpt-oss-20B-medium) with comparable output lengths, particularly excelling in logical reasoning.
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For a comprehensive evaluation of the quality of our reasoning models, we implemented automatic benchmarks to assess their performance including math, code and science. The results indicate **Ring-mini-2.0** achieves comparable performace with **Ring-lite-2507** while activating only half parameters.
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<p align="center">
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<img src="https://mdn.alipayobjects.com/huamei_d2byvp/afts/img/OQWDT7e6BVwAAAAATGAAAAgADod9AQFr/original" width="1000"/>
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## Enhanced Reasoning: Joint Training with SFT + RLVR + RLHF
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Built upon Ling-mini-2.0-base, Ring-mini-2.0 undergoes further training with Long-CoT SFT, more stable and continuous RLVR, and RLHF joint optimization, significantly improving the stability and generalization of complex reasoning. On multiple challenging benchmarks (LiveCodeBench, AIME 2025, GPQA, ARC-AGI-v1, etc.), it outperforms dense models below 10B and even rivals larger MoE models (e.g., gpt-oss-20B-medium) with comparable output lengths, particularly excelling in logical reasoning.
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<p align="center">
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<img src="https://mdn.alipayobjects.com/huamei_d2byvp/afts/img/OQWDT7e6BVwAAAAATGAAAAgADod9AQFr/original" width="1000"/>
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