Xiaomi Unveils MiMo-V2.6: Scaling Reinforcement Learning Compute Up to 1M Context to Unlock End-to-End Self-Improvement
Reinforcement learning (RL) has emerged as the defining post-training paradigm driving frontier foundation models toward autonomous self-improvement, yet scaling RL across multi-step agent environments and million-token sequences introduces profound stability and infrastructure hurdles. Xiaomi's LLM-Core Team releases the comprehensive technical report for MiMo-V2.6 (arXiv:2610.11959), an omni-modal foundation model family that scales RL compute across three critical axes. First, it employs high-throughput asynchronous training processing 1,568 samples and 2.7 to 3.7 billion tokens per optimization step across context windows scaling up to 1M tokens. Second, it orchestrates training across diverse, multi-domain environments spanning code generation, computer vision, and cybersecurity under a heterogeneous mixture of agent harnesses. Third, it implements groupwise agentic grading to mitigate reward hacking and incentivize concise, token-efficient reasoning trajectories. By freezing the MoE router and establishing multi-layered defense barriers, MiMo-V2.6 guarantees rock-solid convergence at scale, while open-sourcing its RL framework, training dynamics, and interactive environments.
Key Takeaways
- Xiaomi releases MiMo-V2.6, scaling reinforcement learning compute across 1,568 samples, 3.7B tokens per step, and up to 1M context
- Introduces groupwise agentic grading to curb reward hacking and compress trajectory token lengths by over 35%
- Freezes MoE routers to eliminate routing collapse and open-sources the complete RL post-training infrastructure