AI

MiMo-V2.6: Scaling Reinforcement Learning Towards Self-Improvement

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 09/10/2026, 06:18:24 UTC.

Briefings

MiMo-V2.6 is an omni-modal reinforcement learning family designed to advance large foundation models toward self-improvement by scaling compute across code, visual, and cyber domains. The system utilizes a hybrid-SWA architecture with frozen MoE routers and multi-layer reward hacking defenses to maintain stability during high-throughput asynchronous training. Key metrics include processing 1,568 samples and 2.7-3.7B tokens per step at context lengths up to 1M tokens, while employing groupwise agentic grading for accurate long-horizon reward signals.

Key points

• MiMo-V2.6 employs a hybrid-SWA architecture with frozen MoE routers and multi-layer reward hacking defenses to maintain training stability at scale.
• The system scales RL compute across code, visual, and cyber domains using asynchronous training that consumes 1,568 samples and 2.7-3.7B tokens per step at context lengths up to 1M tokens.

Context

This report introduces the MiMo-V2.6 series, an omni-modal family pushing the frontier of model intelligence by scaling reinforcement learning compute. It builds on a hybrid-SWA architecture and provides a unified infrastructure for mixed-task agentic RL, enabling reproduction and further research on scaled RL and model self-improvement.

Limitations

Evidence covers only the training methodology and infrastructure; specific performance outcomes, deployment locations, or long-term impact are not reported.

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