2 papers
cs.CL2026
Rethinking Length-Based Training: Batch Composition and Loss Normalization in Speech Token Language Models
Hongjin Song, Runwu Shi, Weiqiao Shan +4
Short-to-long training is a simple curriculum for speech models, but its gains can be difficult to interpret. In speech token language models, length-based training can change the…
cs.CL2026
HRM-Text: Efficient Pretraining Beyond Scaling
Guan Wang, Changling Liu, Chenyu Wang +6
The current pretraining paradigm for large language models relies on massive compute and internet-scale raw text, creating a significant barrier to foundational research. In contra…