Intelligence with Everyone: RL @ MiniMax, with Olive Song, from AIE NYC & Inference by Turing Post

Tight feedback loops between research and applications

MiniMax uniquely builds both foundation models and user-facing applications in-house, allowing cross-functional teams to rapidly identify and fix model weaknesses through direct deployment experience.

Expert developers serve as human reward models

In-house developers actively participate in the training cycle by defining problems, refactoring repos, and providing precise reward signals on which model behaviors are reliable and useful.

Dynamic adaptation through interleaved thinking

M2 interleaves reasoning with tool execution, allowing the model to observe environmental feedback and re-think before acting again across 10-100 turns rather than using single-pass reasoning.

Long-horizon workflow automation

This architecture enables autonomous handling of noisy, dynamic environments and complex multi-tool workflows using Gmail, Notion, and terminals with minimal human intervention.

Perturbation pipelines enforce broad generalization

The team systematically varies training environments across tools, prompts, chat templates, and scaffolds to ensure generalization across the model's entire operational space.

Combatting reward hacking with FP32 precision

To prevent the model from exploiting reward signals, the team runs reinforcement learning at FP32 precision and engages in meticulous debugging of training dynamics.

Small parameter count enables multi-agent scaling

At only 10 billion active parameters, M2 is cost-efficient enough to deploy multiple parallel copies for concurrent research, writing, and analysis tasks.