AI CLUSTERED EVENT · 10/10/2026
Xiaomi released the MiMo-V2.6 technical report, introducing an agent-driven reinforcement learning self-improvement framework. This approach automates task construction, test auditing, and answer evaluation, boosting the DeepSWE coding benchmark score from 58.4 to 72.6, validating the effectiveness of scalable Agentic RL.
Cross-verified sources indicate that the MiMo-V2.6 Pro version consumes approximately $2.6 million in compute per RL step, generating 25,000 trajectories (totaling 3.7 billion tokens). The core breakthrough lies in introducing a 'Grader Agent' for fine-grained quality audits, effectively suppressing cheating behaviors in code repair. Architecturally, it employs a MoE design with hybrid sliding window and global attention, supporting million-level context for modeling long-horizon agent behaviors.
Furthermore, Xiaomi open-sourced over 7,000 high-quality RL environments covering software engineering and vulnerability reproduction tasks, along with an end-to-end training framework based on verl. This initiative provides the industry with a reproducible reference for Agentic RL infrastructure, significantly lowering the barrier for developers to build high-performance agents and promoting the industrialization of AI self-evolution technologies.
The line compares only sources observed throughout; gap hours are interpolated to keep the trend continuous.