Author

Naiqiang Tan

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#artificial intelligence Preprint Oct 2026

Heterogeneous Co-Evolution Learning for Tool-Efficient Search Agents with Zero Labels

Deep search agents extend language models by interleaving reasoning with external retrieval. However, effective search-agent training requires addressing two challenges beyond final-answer optimization. First, the capability frontier of agents is largely determined by training questions, whereas fixed or labeled task c...

Qin-Wen Luo, Sheng Ren, Xiang Chen et al. · 0 citations

Preprint Aug 2026

Rethinking Normalization Placement for LLMs: Post-Norm under Curriculum Depth Growing

Pre-norm is the standard normalization placement in modern Transformers because it facilitates joint optimization of full-depth models. We ask whether this preference persists when depth is introduced through a curriculum. In curriculum depth growth, each appended block receives the boundary representation produced by...

Sheng Ren, Yadong Wang, Naiqiang Tan et al. · 0 citations