1 citations · 3 across the 6 of their papers we have counts for
4 papers · 1 filter
Seed-Prover 1.5: Mastering Undergraduate-Level Theorem Proving via Learning from Experience
Jiangjie Chen, Wenxiang Chen, Jiacheng Du +19
Large language models have recently made significant progress to generate rigorous mathematical proofs. In contrast, utilizing LLMs for theorem proving in formal languages (such as…
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…
Unveiling Downstream Performance Scaling of LLMs: A Clustering-Based Perspective
Chengyin Xu, Kaiyuan Chen, Xiao Li +2
The escalating scale and cost of Large Language Models (LLMs) training necessitate accurate pre-training prediction of downstream task performance for comprehensive understanding o…
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…