The public training run now has released checkpoints
Xiaomi has published MiMo-V2.6-Pro-RL and MiMo-V2.6-Flash-RL on Hugging Face after exposing live telemetry from the reinforcement-learning run that produced the series. The release also includes MiMo-V2.6-Distill-Qwen-9B, a smaller supervised fine-tune of Qwen3.5-9B on MiMo-generated data intended as a starting point for agentic reinforcement-learning research. The earlier live dashboard remains useful provenance for how the run progressed, while the downloadable weights move the work from a training-process demonstration into something outside teams can inspect and serve.
Community discussions on Hacker News and r/LocalLLaMA surfaced the release, but they are discovery evidence only. The material facts come from Xiaomi's own model cards and release page: public checkpoints exist for Pro, Flash and the 9B distill, and Xiaomi provides deployment instructions for SGLang and vLLM.
Pro and Flash trade model scale for serving cost
The Pro model card describes a sparse mixture-of-experts system with 1.02 trillion total parameters and 42 billion activated parameters. Flash uses 309 billion total parameters with 15 billion activated. Both cards specify a one-million-token context window and native text, image, video and audio inputs, with separate vision and audio encoders and a multi-token speculative decoder.
The checkpoints are marked MIT on Hugging Face. Xiaomi publishes concrete serving recipes rather than only hosted API access, including tensor, data and expert parallel settings for Pro and simpler vLLM and SGLang paths for Flash. Hardware requirements remain substantial, especially for Pro, so open weights should not be read as a claim that either model is practical on ordinary consumer hardware.
The release makes the RL recipe more inspectable
Xiaomi says the series used one mixed reinforcement-learning run across coding, general-agent, visual and cybersecurity tasks rather than separate domain-specific runs. Its published recipe uses fully asynchronous Group Relative Policy Optimization with 1,568 prompts and 16 rollouts per step, plus an agentic grading scheme intended to rank successful trajectories rather than reward every passing solution equally. The smaller 9B release exposes a related training artefact, with Xiaomi saying its supervised mixture contains 77.4 billion total tokens and 27.2 billion loss-bearing tokens.
Those details improve reproducibility of the training design but do not make the full run reproducible from weights alone. Xiaomi has not published every environment, grader implementation, trajectory or infrastructure detail needed to recreate the same policy update sequence, and the original live telemetry remains first-party evidence about the run rather than an independent audit.
Benchmark leadership remains a vendor claim until reproduced
Xiaomi publishes broad benchmark tables covering coding agents, general agents, visual tasks and cybersecurity. The same tables show a mixed picture rather than universal leadership: Pro and Flash are competitive on several agent suites, while models from Anthropic and OpenAI remain ahead on some coding and exploit benchmarks. Several MiMo-branded evaluation sets are internal to Xiaomi, which further limits cross-vendor comparison.
The useful next evidence is third-party execution with pinned harness versions, especially long-context tool use, multimodal tasks, inference memory, quantisation quality and throughput. The release confirms the models, architecture, licence and deployment path. Claims that the RL process achieved self-improvement or that MiMo-V2.6 is generally superior to other frontier systems remain Xiaomi's interpretation until those results are independently reproduced.