# Xiaomi Is Streaming MiMo 2.6's Training Run. The Bill Is Clearer Than the Recipe. **Summary:** Xiaomi's live MiMo 2.6 dashboard shows an estimated bill above $1 million, six restarts, broad data categories, and checkpoint scores that sometimes fall. The mess is useful evidence, but it cannot replace the model card. - Canonical: https://markhuang.ai/news/mimo-2-6-live-training-bill-not-recipe - Language: en - Author: [Mark Huang](https://markhuang.ai/about) - Published: 2026-09-16 - Section: News - Tags: Xiaomi MiMo, MiMo 2.6, Reinforcement Learning, AI Transparency, Model Evaluation - Source: [Xiaomi MiMo](https://mimo.xiaomi.com/rl/) - License: https://creativecommons.org/licenses/by-nc/4.0/ --- ![Two luminous AI training corridors run behind observation glass while a sealed chamber remains closed between them](https://cdn.markhuang.ai/news/mimo-2-6-live-training-bill-not-recipe/hero.webp) *Xiaomi has opened a window onto two large training runs. The glass does not reveal everything happening behind it.* [Xiaomi's MiMo 2.6 reinforcement-learning dashboard](https://mimo.xiaomi.com/rl/) is streaming two runs, Pro and Flash, directly from trainer logs. When I checked it on September 16, 2026, the page's live estimate put their combined cost above $1 million. It also showed 15 completed steps for Flash, 10 for Pro, and six restarts between them. I would rather see this than another polished launch chart. The live run shows the expensive, awkward middle of model development. Scores wobble. Infrastructure fails while the meter keeps running. That is useful, but it is not an auditable training recipe. I can watch the work happen without yet knowing enough to reproduce it or judge the finished model. ## The wobble is the useful part The dashboard tracks reward, policy loss, entropy, gradient norm, context length, infrastructure errors, timing, and other trainer metrics. It also breaks the sampled work into code, general, cyber, visual, and chat categories. The site's headline metric is `avg@n`, which Xiaomi defines as the mean share of successful attempts for each prompt in a training step. That metric did not rise neatly. Flash moved from 0.514 at its first reported step to 0.596 at step 15, with several declines along the way. Pro started at 0.565, reached 0.604 at step 9, then fell to 0.590 at step 10. The runs are still live, so the latest points settle nothing. They do show that reinforcement learning is an experiment in progress, not the smooth staircase we usually get after launch. Flash showed one restart and Pro showed five when I captured the page. Xiaomi also posted a notice that the Pro run was restarting because of a VRAM issue on one node. A launch post could easily leave that out. Keeping it visible gives engineers a better sense of the system that produced the model. ## One coding score can move backward Xiaomi has attached one offline evaluation so far: DeepSWE v1.1 through mini-swe-agent, reported as an average of three runs. Flash's published scores ranged from 48.67 at step 1 to 60.77 at step 12, but individual checkpoints sometimes dropped after an earlier gain. Pro reached 62.24 at step 8 after its own uneven sequence. [DeepSWE's public repository](https://github.com/datacurve-ai/deep-swe) describes 113 original software-engineering tasks across five programming languages. Its authors also make the boundary clear: the benchmark measures long-horizon repository work under a fixed harness, not overall model quality or the quality of a vendor's finished coding product. Xiaomi's live chart is therefore interesting evidence about checkpoint behavior, not proof that MiMo 2.6 will be the best coding agent. The dashboard is most useful when a later score falls. A single launch number hides checkpoint selection and run-to-run variance, which makes training progress easy to mistake for a guaranteed product improvement. I made the same argument in [my piece on model build-offs](https://markhuang.ai/news/model-build-offs-need-failure-rates): the pattern of misses and variance tells me more than a trophy result. ## The dashboard stops short of an audit The live page says the MiMo 2.6 series is coming soon. It does not yet provide an architecture, parameter count, training-data recipe, license, safety evaluation, release date, or model card. It names broad data categories and exposes per-step activity, but those labels do not tell me which datasets were used, how they were filtered, what reward signals shaped behavior, or what risks were tested before deployment. That absence may simply reflect timing. This is a training dashboard, not the release package. Xiaomi's previous [MiMo 2.5 release](https://mimo.mi.com/docs/en-US/news/latest/v2.5-open-sourced) later included MIT-licensed weights and base models, a one-million-token context claim, and deployment links. I would give MiMo 2.6 the same chance to arrive with proper documentation. I would not award that documentation in advance because a live graph looks unusually open. A small [r/LocalLLaMA thread](https://www.reddit.com/r/LocalLLaMA/comments/1wi9ebm/xiaomi_mimo_26_live_training_dashboard/) split between enthusiasm for the visibility and discomfort at seeing a seven-figure bill beside a benchmark decline. That is far too little discussion to call consensus. The skeptical comment still explains why I kept looking at the page: it shows an outcome that a launch deck might omit. > **Info:** > > I would use the live dashboard as evidence about the training process. The eventual model card, weights, evaluation artifacts, and independent tests will be the evidence about the model. ## Keep the stream after launch I want more labs to publish this kind of operational record. Costs, restarts, checkpoint movement, and infrastructure failures make model development look less inevitable. Xiaomi deserves credit for putting those details in public while the runs can still go wrong. The harder test comes at release. I will look for the exact model variants, architecture, license, data and reward-method description, safety results, benchmark settings, and downloadable evaluation artifacts. I also want the live dashboard to remain available as a dated record rather than disappear behind the finished score. For now, I can see what the MiMo 2.6 runs cost and how their curves move. Whether I can trust the product will depend on what Xiaomi publishes when the training ends.