How the Forge RL Framework Solves Scalable Agent Reinforcement Learning’s Impossible Trinity

  Forge: Breaking the Impossible Trinity of Scalable Agent Reinforcement Learning – The RL Framework and Algorithmic Practice Behind MiniMax M2.5 Abstract MiniMax’s self-developed Forge Reinforcement Learning (RL) framework resolves the throughput-stability-flexibility trinity plaguing scalable agent RL through middleware architecture, Windowed FIFO scheduling, Prefix Tree Merging and other innovations. It achieves a 40x training speedup and underpins the large-scale real-world deployment of the MiniMax M2.5 model. Have you ever wondered why large-scale Reinforcement Learning (RL) has long struggled to find practical application in complex real-world agent scenarios? The core roadblock lies in an impossible trinity: boosting system throughput often comes …