MiMo-V2.6: Scaling Reinforcement Learning Towards Self-Improvement (Oct 2026)

Title: MiMo-V2.6: Scaling Reinforcement Learning Towards Self-Improvement (Oct 2026) Link: http://arxiv.org/abs/2610.11959v1 Date: October 2026 Summary: Presents MiMo-V2.6, an omni-modal model family developed through large-scale agentic reinforcement learning. The report details scaling RL across training compute, diverse task environments and harnesses, and groupwise agentic grading, supported by asynchronous infrastructure, reward-hacking safeguards, and router freezing. It reports gains across coding, general workflows, visual tasks, and cybersecurity, and releases a distilled model, environments, verifiers, and an RL framework. Key Topics: Agentic reinforcement learning at scale Multimodal mixture-of-experts models Groupwise agentic grading and advantage redistribution RL environments and multi-harness training Reward-hacking mitigation Asynchronous RL infrastructure and training–inference consistency Open-source models, environments, and tooling Chapters: 00:00 - MiMo-V2.6 Overview 01:02 - Scaling Long Context 03:03 - Model Architecture 04:03 - Stabilizing Large-Scale Training 05:55 - Scaling RL Compute 07:15 - Building Diverse Environments 08:29 - Multi-Harness Training 09:56 - Preventing Reward Hacking 12:56 - Improving Automated Grading 15:50 - Stabilizing MoE Routing 17:27 - Aligning Training And Inference 19:06 - Enforcing Concise Solutions 20:01 - Open-Source Releases 20:31 - Results And Limitations 21:56 - Distillation For Open-Ended Tasks Footage credits: cottonbro studio: https://www.pexels.com/video/running-... Andy Barbour: https://www.pexels.com/video/a-man-co... Pavel Danilyuk: https://www.pexels.com/video/man-and-... AI-generated narration. Footage marked illustrative is context, not evidence of the paper's results.