4 citations · 7 across the 6 of their papers we have counts for
4 papers · 1 filter
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…
Scaling Latent Reasoning via Looped Language Models
Rui-Jie Zhu, Zixuan Wang, Kai Hua +30
Modern LLMs are trained to "think" primarily via explicit text generation, such as chain-of-thought (CoT), which defers reasoning to post-training and under-leverages pre-training…
SuperGPQA: Scaling LLM Evaluation across 285 Graduate Disciplines
P Team, Xinrun Du, Yifan Yao +94
Large language models (LLMs) have demonstrated remarkable proficiency in mainstream academic disciplines such as mathematics, physics, and computer science. However, human knowledg…
GenDec: A robust generative Question-decomposition method for Multi-hop reasoning
Jian Wu, Linyi Yang, Yuliang Ji +3
Multi-hop QA (MHQA) involves step-by-step reasoning to answer complex questions and find multiple relevant supporting facts. However, Existing large language models'(LLMs) reasonin…