3 papers
cs.AI2026
Scaling Domain Data Repetition in LLM Pretraining
Jingwei Li, Xinran Gu, Rui Dai +5
As large language models scale, their training-token budgets must also increase to maintain an appropriate tokens-per-parameter ratio (\(\mathrm{TPP}\)). However, high-quality doma…
cs.CL2025
Reformulation for Pretraining Data Augmentation
Xintong Hao, Ruijie Zhu, Ge Zhang +2
Despite the impressive capabilities of large language models across various tasks, their continued scaling is severely hampered not only by data scarcity but also by the performanc…
cs.CL2025
Seed1.5-Thinking: Advancing Superb Reasoning Models with Reinforcement Learning
ByteDance Seed, :, Jiaze Chen +267
We introduce Seed1.5-Thinking, capable of reasoning through thinking before responding, resulting in improved performance on a wide range of benchmarks. Seed1.5-Thinking achieves 8…