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cs.LG2026
How Does Local Landscape Geometry Evolve in Language Model Pre-Training?
Zhanpeng Zhou, Yuhan Sun, Bingrui Li +4
The scale and expense of pre-training language models make efficient hyperparameter tuning essential, yet a principled guidance is still missing. In this work, we analyze language…
cs.LG2026
Loop-P: Residual Scaling and Hyperparameter Transfer in Looped Transformers
Shaowen Wang, Bingrui Li, Ge Zhang +4
Looped Transformers have drawn growing attention because looping improves multi-step reasoning and increases the executed depth of a model without adding parameters. They achieve t…
cs.LG2026
Balanced Aggregation: Understanding and Fixing Aggregation Bias in GRPO
Zhiyuan Zeng, Jiameng Huang, Zhangyue Yin +8
Reinforcement learning with verifiable rewards (RLVR) has become a central paradigm for improving reasoning and code generation in large language models, and GRPO-style training is…