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Articles

[2601.12720] Teaching Large Reasoning Models Effective Reflection

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By Jingwei Song, Jinpeng Li, Qi Zhu, Ganqu Cui
| arxiv.org
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Open in Who Shared View Duplicates (526) Issue with article?

UltraIF: Advancing Instruction Following from the Wild

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By Kaikai An, Ganqu Cui, Shuzheng Si, Ning Ding
| huggingface.co
Papers Published on Feb 6 · Submitted by kkk-an on Feb 7 Abstract Instruction-following made modern large language models (LLMs) helpful assistants. However, the key to taming LLMs on complex instructions remains mysterious, for that there are huge gaps between models trained by open-source community and those trained by leading companies. To bridge the gap, we propose a simple and scalable approach UltraIF for building LLMs that can follow complex instructions with open-source data.
Open in Who Shared Issue with article?

[2403.08281] Mastering Text, Code and Math Simultaneously via Fusing Highly Specialized Language Models

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By Yulin Chen, Ganqu Cui, Ruobing Xie, Xingtai Lv
| arxiv.org
Happy π Day! Pi Day is finally here – and so is Giving Day for arXiv! Donate today to directly support arXiv initiatives and help keep open science open. Infinite decimals. Infinite new ideas to discover. Infinite reasons to give.
Open in Who Shared View Duplicates (686) Issue with article?
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