@sleepy0x13: Luo Fuli just wrote a very long retrospective on MiMo-V2.6. Reading through the whole thing, it’s actually addressing a…

Luo Fuli just wrote a very long retrospective on MiMo-V2.6. Reading through the whole thing, it’s actually addressing a question that’s extremely critical right now:

Pre-training’s Scaling Law has been running for so many years now—can RL do the same? Keep piling on the compute, keep adding tasks, put the model into more real-world environments—can its capabilities keep climbing?

Xiaomi has basically been betting on this for the past six months.

The RL scale of MiMo-V2.6 is already ridiculously massive. A single update pulls 1,568 tasks, each running 16 rollouts in parallel, which means one step generates roughly 25,000 agent trajectories and burns through billions of tokens. Both Pro and Flash ended up running 30 steps.

What Luo Fuli and team discovered is that the real challenge in scaling Agent RL isn’t just the training itself anymore.

The first issue is, you need enough “worlds” for the model to step into.

Math RL is simple: one problem, one answer, and the model knows right away if it’s correct. Agents are different. They have to write code, open browsers, operate computers, call tools, check results, then decide the next move.

So Xiaomi expanded Environments alongside this.

They built over 7,000 RL Task Environments, covering Coding, General Agent, Visual Agent, Cybersecurity, and other task types. A single trajectory might last dozens or even hundreds of steps, with the model truly having to complete things inside the environment.

The second change is even more noteworthy:

Harness got folded right into the training.

In the past, everyone treated Harness as something outside the model. Model companies handled training the model, then products like Claude Code, Codex, and various Agent setups layered on System Prompts, Tools, Memory, and Context Management externally.

MiMo-V2.6 threw different Harnesses straight into the same RL round.

For the same model, one trajectory might work in one kind of Coding Harness, the next switching to a whole new toolset and Context management approach. Xiaomi mixed Coding, Visual, general Agent, and security tasks all together in training—what they call internally You Only RL Once.

They don’t want the model to just learn to adapt to one specific Agent product; they want it to learn a capability that transfers across Harnesses.

I think this is the most noteworthy point in the whole piece.

Harness is starting to burrow from being a post-launch “shell” into the model’s training itself.

The third issue is trickier: once the Agent finishes a task, how do you know if it actually did a good job?

Take fixing a bug, for example.

Out of 16 rollouts, maybe all pass the test in the end—but one person changed just three lines of code, another added dozens of lines in a patch; one found the issue with five tool calls, another burned through over 100,000 tokens by sheer luck.

If the Reward is just “pass/fail,” all these outcomes look basically identical to RL.

So Xiaomi dumped a ton of compute into the Grader.

Multiple successful trajectories from the same task get pulled out and compared: which solution is cleaner, which path shorter, which uses fewer tokens—then Rewards get reassigned.

They even have to specifically combat Reward Hacking.

Because once the model figures out that “how to fool the grader” is easier than actually completing the task, it’ll absolutely do it. So the training environment has to keep patching leaks, scrubbing answer leaks, running anomaly detection, and even dispatching Agents to actively attack its own training setup.

At this point, Agent RL is starting to look less and less like the “reinforcement learning” we used to understand.

What you’re truly scaling becomes a whole system:

Rollout, Environment, Harness, Grader.

MiMo-V2.6 Pro saw a huge jump over the previous generation on Coding and Agent Benchmarks. Xiaomi also released Pro, Flash, a Qwen3.5-9B Agent model trained on MiMo trajectories, over 7,000 RL Environments, the RL Framework, and mini-harnesses all together.

But these are just the outcomes.

What this piece really reveals is that the training methods for Frontier Models might be undergoing a massive shift.

In the past, scaling models meant counting parameters, tokens, and GPUs.

Now models are starting to have experiences.

Throw it into thousands of environments, let it run full tasks on its own, fail, retry, then simultaneously try dozens of approaches. Then spend another chunk of compute to judge which of those experiences are worth remembering.

So the truly expensive thing in the future might not just be a bigger Base Model.

A model company will also need the ability to keep churning out tasks, building environments, maintaining Harnesses, running hundreds of thousands of agent trajectories, then sifting out the best experiences to feed back into the model.

I’d call it an Experience Factory.

Training models together with Harnesses, Synthetic Environments, Computer Use, RL Graders, Long-horizon Agents—these look like separate tech paths on the surface, but underneath, they’re all tackling the same problem:

How to give the model more and more things it’s truly completed.

It’s still too early to talk about “Self-Improvement.” Environments are still human-built, Rewards are still human-designed—what’s worth learning still has boundaries drawn by people.

But MiMo-V2.6 at least proves one thing: the next round of scaling for large models isn’t just happening in pre-training clusters anymore.

It’s happening in every world the model has ever experienced.

Fuli Luo (@_LuoFuli): MiMo-V2.6: The Hard Road to Scaling Up RL

MiMo-V2.6 is very likely one of the largest single RL runs, by compute, that any open-source model team has undertaken to date. In an era when compute is brutally scarce, we still chose to dedicate a team of several dozen people to one