view article Article Aligning to What? Rethinking Agent Generalization in MiniMax M2 27 days ago • 25
WebExplorer: Explore and Evolve for Training Long-Horizon Web Agents Paper • 2509.06501 • Published Sep 8 • 78
Hephaestus: Improving Fundamental Agent Capabilities of Large Language Models through Continual Pre-Training Paper • 2502.06589 • Published Feb 10 • 21
MiniMax-M1 Collection MiniMax-M1, the world's first open-weight, large-scale hybrid-attention reasoning model. • 6 items • Updated Oct 21 • 116
MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention Paper • 2506.13585 • Published Jun 16 • 271
SynLogic: Synthesizing Verifiable Reasoning Data at Scale for Learning Logical Reasoning and Beyond Paper • 2505.19641 • Published May 26 • 67
One-RL-to-See-Them-All Collection One RL to See Them All: Visual Triple Unified Reinforcement Learning. GitHub: https://github.com/MiniMax-AI/One-RL-to-See-Them-All • 5 items • Updated Jun 10 • 29
One RL to See Them All: Visual Triple Unified Reinforcement Learning Paper • 2505.18129 • Published May 23 • 60
MiniMax-Speech: Intrinsic Zero-Shot Text-to-Speech with a Learnable Speaker Encoder Paper • 2505.07916 • Published May 12 • 133
MiniMax-01: Scaling Foundation Models with Lightning Attention Paper • 2501.08313 • Published Jan 14 • 301