Xiaomi AI Training Live: Insights from the MiMo 2.6 RL Dashboard
benchmarks training
| Source: Mastodon | Original article
Xiaomi AI publicly trains its MiMo 2.6 model, displaying a live dashboard that tracks trainer logs, a $1 million cost counter, seven restarts and a benchmark line.
Xiaomi’s AI research team has opened its reinforcement‑learning (RL) training of the MiMo 2.6 language model to public view, launching a live dashboard that streams trainer logs, a running cost counter and performance benchmarks. The site, hosted at mimo.xiaomi.com/rl, shows two parallel runs – MiMo‑v2.6‑pro and MiMo‑v2.6‑flash – and updates in real time with metrics such as tokens processed (between 2.22 billion and 2.81 billion), the number of training prompts (1,568) and the 16 rollouts per step that drive the RL loop. A visible cost meter has already topped $1 million, while other reports from the same period note total spend exceeding $3 million. The dashboard also records seven automatic restarts and plots a benchmark line that climbs as the model improves.
The move matters because it pushes transparency into a stage of AI development that is usually hidden behind internal compute farms. By exposing the financial and computational footprint of large‑scale RL fine‑tuning, Xiaomi invites scrutiny of efficiency, safety and reproducibility. The live view also offers the broader research community a rare glimpse of how token‑level feedback, human‑in‑the‑loop prompts and rollout strategies translate into measurable gains, potentially informing best practices for other firms that are scaling similar pipelines.
Going forward, observers will watch for the final benchmark scores that the dashboard promises to display, as well as any public release of the MiMo 2.6 model or its training code. Xiaomi’s leader of the MiMo team, Fuli Luo, hinted that the experiment is part of a broader inquiry into the limits of RL for language models, so subsequent updates may include comparative studies, cost‑efficiency analyses or extensions of the live‑monitoring approach to future model generations.
Sources
Back to AIPULSEN