7 papers
Pretraining Language Models on Historical Text
Xiaoxi Luo, Zachary Shinnick, Niclas Griesshaber +5
We introduce TypewriterLM, a 7.24B History language model (LM) trained exclusively on English text predating 1913. Developing History LMs requires addressing challenges in data qua…
CARE-RL: Capability-Aware Reinforcement Learning for Mitigating Cross-Domain Conflicts
Rui Zhang, Xinle Wu, Yao Lu
Reinforcement learning (RL) with verifiable rewards has achieved strong progress in reasoning-oriented LLMs, but extending it to multi-domain RL remains challenging due to reward u…
Learning from Failures: Correction-Oriented Policy Optimization with Verifiable Rewards
Mengjie Ren, Jie Lou, Boxi Cao +6
Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as an effective paradigm for improving the reasoning capabilities of large language models. However, RLVR training…
PowerStep: Memory-Efficient Adaptive Optimization via -Norm Steepest Descent
Yao Lu, Dengdong Fan, Shixun Zhang +1
Adaptive optimizers, most notably Adam, have become the default standard for training large-scale neural networks such as Transformers. These methods maintain running estimates of…
AscendKernelGen: A Systematic Study of LLM-Based Kernel Generation for Neural Processing Units
Xinzi Cao, Jianyang Zhai, Pengfei Li +17
To meet the ever-increasing demand for computational efficiency, Neural Processing Units (NPUs) have become critical in modern AI infrastructure. However, unlocking their full pote…
Automatic Configuration of LLM Post-Training Pipelines
Channe Chwa, Xinle Wu, Yao Lu
LLM post-training pipelines that combine supervised fine-tuning and reinforcement learning are difficult to configure under realistic compute budgets: the configuration space is hi…