4 papers
REER-PT: Reverse-Engineered Reasoning for Perplexity-Guided Pre-training Data Augmentation
Haoran Que, Jiajun Shi, Ting Huang +7
As language-model compute continues to scale, high-quality training data is becoming an increasingly important bottleneck. Conventional next-token prediction supervises what follow…
On the Residual Scaling of Looped Transformers: Stability and Transferability
Shaowen Wang, Bingrui Li, Ge Zhang +3
Looped (weight-tied) Transformers apply a shared residual block times (, same at each step), increasing effective depth without adding p…
Parallel Loop Transformer for Efficient Test-Time Computation Scaling
Bohong Wu, Mengzhao Chen, Xiang Luo +9
Large Language Models (LLMs) are powerful but often too slow and costly for real-world use during inference. Looped transformers save on parameters by reusing the same weights for…
Efficient Pretraining Length Scaling
Bohong Wu, Shen Yan, Sijun Zhang +4
Recent advances in large language models have demonstrated the effectiveness of length scaling during post-training, yet its potential in pre-training remains underexplored. We pre…