3 citations · 12 across the 17 of their papers we have counts for
5 papers · 1 filter
LIMI: Less is More for Agency
Yang Xiao, Mohan Jiang, Jie Sun +18
We define Agency as the emergent capacity of AI systems to function as autonomous agents actively discovering problems, formulating hypotheses, and executing solutions through self…
OctoThinker: Mid-training Incentivizes Reinforcement Learning Scaling
Zengzhi Wang, Fan Zhou, Xuefeng Li +1
Different base language model families, such as Llama and Qwen, exhibit divergent behaviors during post-training with reinforcement learning (RL), especially on reasoning-intensive…
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,…
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…
LIMR: Less is More for RL Scaling
Xuefeng Li, Haoyang Zou, Pengfei Liu
In this paper, we ask: what truly determines the effectiveness of RL training data for enhancing language models' reasoning capabilities? While recent advances like o1, Deepseek R1…