Reinforcement learning (RL) is the central training paradigm for advancing large foundation models towards self-improvement.
This report introduces the MiMo-V2.6 series, an omni-modal family that pushes the frontier of model intelligence by scaling RL compute.
Prior to RL, we conduct mid-training on a broad multimodal corpus to provide ample exploration space, and build a solid infrastructure on the pretrained hybrid-SWA architecture to support subsequent scale-up.
Scaling RL Compute
We scale RL compute along three dimensions:
- Larger batches and higher throughput, with an asynchronous training that consumes 1,568 samples and 2.7-3.7B tokens per step at context lengths of up to 1M;
- More diverse and complex environments, spanning code, general, visual, and cyber domains under a mixture of agent harnesses;
- More grader compute, via groupwise agentic grading that yields more accurate reward signals for long-horizon tasks and steers the model towards shorter, more token-efficient solutions.
Stability and Infrastructure
To keep training stable at scale, we freeze the MoE router and establish a multi-layer defense against reward hacking.
We further build infrastructure for mixed-task agentic RL, including:
- A unified trajectory representation;
- High-concurrency multi-framework rollout;
- Decoupled control and data planes;
- Training-inference consistency.
Open Source Contributions
We open-source the training dynamics, RL environments, and RL framework to facilitate reproduction and further research on scaled RL and model self-improvement.