most citedKimi k1.5: Scaling Reinforcement Learning with LLMs

12 citations · 12 across the 1 of their papers we have counts for

collaborators

2 papers

cs.LG2025

MoBA: Mixture of Block Attention for Long-Context LLMs

Enzhe Lu, Zhejun Jiang, Jingyuan Liu +22

Scaling the effective context length is essential for advancing large language models (LLMs) toward artificial general intelligence (AGI). However, the quadratic increase in comput…

cs.AI2025★ 12 cited

Kimi k1.5: Scaling Reinforcement Learning with LLMs

Kimi Team, Angang Du, Bofei Gao +93

Language model pretraining with next token prediction has proved effective for scaling compute but is limited to the amount of available training data. Scaling reinforcement learni…