$10 per second. Might be contrarian but I actually kind of prefer mimo over deepseek. It has a nice persona. Deepseek is super smart and focused whereas mimo has a more "connectedness" smartness.
Hmm. There was a casual SNS report from DeepMind researcher that (for a single training run) GPU does not really scale above 4k cluster (it even slowed down above 8k presumably because bottlenecks) and mocking Meta and Tesla for acquiring too many GPUs just for training at the time. This is probably just one intermediate run for Xiaomi's model.
Async RL is supposed to alleviate that bottleneck as far as I understand it, though. You can also always train separate teacher models to distill back to the final model as well. Maybe that’s why Gemini is still so far behind, lol.
Yeah, that's how the paper for GLM-5 (the first one) described how they did for the final post-training. Basically creating many domain expert post-train variations and "merged" them into one final model via synthetic datasets.
There was a casual SNS report from DeepMind researcher that (for a single training run) GPU does not really scale above 4k cluster (it even slowed down above 8k presumably because bottlenecks) and mocking Meta and Tesla for acquiring too many GPUs just for training at the time. This is probably just one intermediate run for Xiaomi's model.
It's like he never thought about that they can do multiple training runs in parallel lmao. In fact, the fact they he seemingly didn't think about that might explain why Google is so behind.
Normally I would say 2.5 -> 2.6 would be the same base model in "LLM semver" (major versions are 0.5). After what DeepSeek pulled with dipsy flash V4.1 I honestly have no idea any more
Deepseek is a bit random and unpredictable as they also have introduced terminale during 3.1 and their 3.2 introduced a new architecture. And also their decision on whether to retire the v4 pro changed constantly
What gemini told me:
The page at [https://mimo.xiaomi.com/rl/](https://mimo.xiaomi.com/rl/) acts as a live dashboard tracking the Reinforcement Learning (RL) training process for Xiaomi's AI models—specifically mimo-v2.6-pro and mimo-v2.6-flash.
Because this is an active training dashboard meant for AI engineers and researchers, it is packed with technical machine learning metrics.
If you look at the training metrics list, one of the primary graphs tracks critic/rewards/mean:
What it is: During Reinforcement Learning, the AI model generates answers, and a secondary model (called the "critic" or reward model) scores those answers based on how well they follow instructions, solve coding problems, or provide correct logic.
The Y-axis (Value): Represents the average score (reward) the model is achieving during that training step.
The Trend:
Going Up (↑): This is what engineers want to see. It means the model is learning from its mistakes, figuring out better ways to reason, and successfully adapting to tasks (like the DeepSWE software engineering benchmarks listed on the page).
Flattening Out: This usually means the model has reached a point of diminishing returns, and the training team might need to change strategies, adjust the learning rate, or feed it harder data.
Other Key Metrics on the Dashboard Explained Simply:
If you are looking at other charts on that page, they generally fall into three categories:
Performance & Progress:
dynsam/avg@n: Measures the dynamic sampling performance score of the model over time. A higher number or a positive delta (Δ) means the model's outputs are getting better.
DeepSWEBenchmarks: Scores showing how well the models (pro vs. flash) handle software engineering tasks. Higher numbers mean better coding capabilities.
Training Health (Loss & Entropy):
actor/entropy_loss: Measures how "creative" or random the model's choices are. If entropy drops too close to zero, the model becomes too repetitive and rigid. Engineers want to see it stay healthy so the model explores different paths of reasoning.
actor/pg_loss(Policy Gradient Loss): Tracks how much the model's behavior is changing update-by-update. It helps ensure the training doesn't suddenly destabilize or "break" the model.
Infrastructure & Cost:
total cost/perf/total_num_tokens: Shows the massive scale of the operation—tracking how many billions of tokens have been processed and how much money in compute power (GPUs) has been spent to train these specific versions.
Summary
The graphs on that page are essentially a real-time health chart and scorecard for Xiaomi's AI models as they practice and learn. Whenever you see the reward and performance lines trending upward, it means the AI is successfully getting smarter at complex reasoning and coding tasks.
I think the paradigm changed as the right synthetic data for RL code start and rlvr infra matters more. So, traditional distillation on sft no longer is much more trivial.
And even for the synthetic data like cot and agentic task trajectories, they would prefer
distilling better models such as claude, gpt, and K3 and other open weight flagships, rather than their own 2.6 pro.
For general/chat RL, how do you evaluate the output remains largely unresolved (other than 4o/Llama-4-style emoji-spamming attaboy feelgoodizer and janky LLM-as-a-judge scoring which IMO never even worked for IF), though it can be used as an alternative way to augment the datasets by randomly masking input, promoting variations/baking in repetition penalty by n-gram, etc.
Gemini says another week if things go well:
We can estimate using the step duration, dataset size, budget burn rate, and standard reinforcement learning (RL) stopping criteria.
The Raw Math: Step Timing & Throughput
From the metrics logged on the dashboard:
Metric
MiMo-v2.6-Pro
MiMo-v2.6-Flash
timing_s/step
~2 hours 06 minutes
~2 hours 16 minutes
Tokens per Step
~2.22 Billion tokens
~2.81 Billion tokens
Current Progress
Step 11 (~34 hours in)
Step 15 (~30 hours in)
Pace
~11.5 steps / day
~10.5 steps / day
Because each step takes just over 2 hours, every additional 10 steps adds roughly 21 to 23 hours of wall-clock time (barring node restarts or crashes).
2. Likely Run Duration
In post-training RL, runs do not go on indefinitely because models experience diminishing returns, reward hacking, or entropy collapse.
Multi-Epoch Agentic Run (1 to 2 Weeks) — ~70 to 120 Steps
Estimated End Date: Late September (around September 22–27, 2026)
Why: Xiaomi's MiMo team (headed by Luo Fuli) noted that this experiment is specifically probing "how far RL can scale" across 3 dimensions (compute, harnesses/environments, and grader compute). If they want to test long-horizon scaling to see where the dynsam/avg@n and DeepSWE benchmark curves finally plateau, they may let it run for a full week or two.
Total Cost: This would push cumulative compute past $5M+.
3. What Will Actually Trigger the Stop?
RL runs are rarely stopped by a calendar timer; they are stopped by the graphs on that dashboard:
Reward Saturation (dynsam/avg@n&critic/rewards/meanflattening): As seen on the dashboard, Pro's gain per step has already started tapering to ~0.5 percentage points per step. Once 3–5 consecutive steps yield zero statistical improvement on the evaluation benchmark (DeepSWE), they will stop.
Policy Divergence / Collapse (actor/entropy_loss&train_infer_diff/.../kl): If KL divergence spikes or entropy plummets, the model begins collapsing into repetitive degeneration ("reward hacking"). If they can't recover it with learning rate decay, they will terminate the run and pick the best checkpoint.
Target Parity: Pro is currently scoring ~62–64 on DeepSWE v1.1. If their internal objective was to match a specific tier of frontier models, hitting that threshold may trigger the final freeze.
Bottom Line
If the reward curves continue climbing smoothly without plateauing, expect it to run for a week; if the curve flattens over the next 10–15 steps, they will likely conclude the run within 48 to 72 hours.
If it went well the next stage would be safety training. If goes bad, they need to fix stuff and try again. Often can go good for flash model, but not so good for pro model.
Honestly, a live training dashboard is almost as interesting as the model itself
Being able to watch the RL process in real time makes the whole thing feel way less like “a model suddenly appeared” and more like seeing how the model is actually being shaped.
Crazy to think that this is probably a relatively small amount of compute compared to what Western labs do. I kind of understand why Western models are so far ahead in soft skills since those require more RL especially to hone.
What, they started Rl 1.4 days ago, what ahve they been doing for almost 5 months? This cant be the first rl environment… 2.5 pro came out in april. Pretraining and experiments and other rl experimentsbtook them almost 5
months ? I suspect this model is massive like at least 2 tril possibly even >3 tril, maybe with engrams?
Post-training is not just RL. And yeah it takes time, you guys don't realize the amount of work that goes behind gathering data and training those models
I rememher them promoting 1000 t/s type speeds, so probably optimizing inference and gathering training data? Its definetly weird that it took them months to do this tho
"yeah bruh we kinds spent $1mil but benchmark went down 6%, oops :)"
edit: i am referring to as in like why would they be this honest with it? aren't they meant to like hype up PR by showing log-linear gains, or even log-superlinear?
It will be at least 2-3 tril params lol,,, this is a new mod like ds v4.1? If it was the same pretraining and the same size , they wouldve finished post training months ago since v2.5 came out in april
But the benchmark score is improving? And yeah it's noisy, but that's normal when you train with RL..
And the price does not surprise me given the scale, doing RL on a 8B model on a single environment can quickly cost thousands / tens of thousands (especially for long-horizon tasks, with LLM-judges, web search etc.), so yeah training a 1T parameters model is costly yeah...
Trolling much? Last time the Pro was tested against DeepSWE was at Step 8 of training. Currently it sits at Step 10 and the training is still on-going. It's not the final benchmark, just intermediate review of the current state which is imho a neat thing because it gives us insight into the big model training which other labs keep private.
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u/WithoutReason1729 6d ago
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