6 papers
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…
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…
Seed-Prover: Deep and Broad Reasoning for Automated Theorem Proving
Luoxin Chen, Jinming Gu, Liankai Huang +33
LLMs have demonstrated strong mathematical reasoning abilities by leveraging reinforcement learning with long chain-of-thought, yet they continue to struggle with theorem proving d…
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…
Seed1.5-VL Technical Report
Dong Guo, Faming Wu, Feida Zhu +194
We present Seed1.5-VL, a vision-language foundation model designed to advance general-purpose multimodal understanding and reasoning. Seed1.5-VL is composed with a 532M-parameter v…
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…