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20242026
most citedO1 Replication Journey: A Strategic Progress Report -- Part 1

3 citations · 8 across the 16 of their papers we have counts for

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7 papers · 1 filter

cs.CL2026

DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data

Zhen Huang, Yikun Wang, Shijie Xia +1

Pretraining data processing is critical to the downstream performance of Large Language Models (LLMs). However, many existing approaches define a fixed processing strategy at the c…

cs.CL2026

UMoE:Unlocking Every Expert in Domain-Specific Training

Xuefeng Li, Pengfei Liu

Mixture-of-Experts (MoE) models scale capacity without proportional compute cost and have become a key architecture for frontier large language models (LLMs). Yet domain-specific p…

cs.CL2025

Generative AI Act II: Test Time Scaling Drives Cognition Engineering

Shijie Xia, Yiwei Qin, Xuefeng Li +11

The first generation of Large Language Models - what might be called "Act I" of generative AI (2020-2023) - achieved remarkable success through massive parameter and data scaling,…

cs.CL2025★ 2 cited

ToRL: Scaling Tool-Integrated RL

Xuefeng Li, Haoyang Zou, Pengfei Liu

We introduce ToRL (Tool-Integrated Reinforcement Learning), a framework for training large language models (LLMs) to autonomously use computational tools via reinforcement learning…

cs.CL2024★ 1 cited

O1 Replication Journey -- Part 2: Surpassing O1-preview through Simple Distillation, Big Progress or Bitter Lesson?

Zhen Huang, Haoyang Zou, Xuefeng Li +7

This paper presents a critical examination of current approaches to replicating OpenAI's O1 model capabilities, with particular focus on the widespread but often undisclosed use of…

cs.CL2024

SAFETY-J: Evaluating Safety with Critique

Yixiu Liu, Yuxiang Zheng, Shijie Xia +4

The deployment of Large Language Models (LLMs) in content generation raises significant safety concerns, particularly regarding the transparency and interpretability of content eva…

cs.CL2024

Progress or Regress? Self-Improvement Reversal in Post-training

Ting Wu, Xuefeng Li, Pengfei Liu

Self-improvement through post-training methods such as iterative preference learning has been acclaimed for enhancing the problem-solving capabilities (e.g., mathematical reasoning…