Nirmal Krishnan
Chakra Team
Summary
RL environment quality isn’t defined by feature completeness but by task coverage. Perfect simulators don’t scale, and building everything slows progress. Chakra treats environment development like a factory: identify core workflows, build deeply within them, and use strong tasks as the quality gate. The goal isn’t replica fidelity — it’s producing difficult, realistic, verifiable tasks that actually train better models.
Overview
Building RL environments is expensive. Perfect simulators don't scale, and scaling simulators aren't perfect. The way out isn't compromise - it's being precise about what quality means.
We run environment development like a factory: scope to core workflows, build deep within them, let task quality be the gate, and move on.
The RL environment space has a seductive vanity metric: feature coverage. "We replicated 95% of Salesforce." "Our Notion clone supports full database properties, permissions, and templates." Sounds impressive.
But feature coverage doesn't train models. Tasks do.
In our last piece, we argued that environments are delivery mechanisms. That labs aren't buying environments, they're buying tasks. This is the natural follow-up: if tasks are the product, then how you build environments should be entirely in service of producing the best possible tasks. Not the most complete replica.
Henry Ford didn't build a factory that could make everything. He built one that could make one thing extraordinarily well. The Model T came in "any color, so long as it's black." RL environment development has the same choice to make.
The vanity trap
Today, some environment teams spend months building pixel-perfect settings pages, admin panels, and obscure UI surfaces that no task ever exercises and no model ever learns from. The environment looks complete. Completeness is always something to strive towards but it comes at a cost.
The sanity metric is task coverage. How many difficult, realistic, reliably verifiable, hard to hack tasks can this environment support? That's the only number that matters to a lab running RL, and the only number we care about at Chakra.
Why perfect simulators don't scale
In an ideal world, you'd build a 1:1 replica of every enterprise app. Full feature parity. Every edge case modeled.
In practice, the bottleneck isn't engineering talent. It's that "build everything" is the wrong spec. Here's what that actually costs:
UI/UX fidelity: constant side-by-side review against live production apps, which themselves change. You're always playing catch-up.
Process overhead: PRDs, design specs, and QA cycles for features that will never appear in a task
The redesign problem: you spend months modeling a site and it ships a full redesign halfway through. Your work is obsolete before it ships.
Time: at full fidelity, you're delivering maybe 1-2 environments a quarter. Labs are running training loops now.
The Factory Floor
There’s a better way: run environment development like a production line. A repeatable process where every stage is oriented around the metric that actually matters.
Intake. Don't start with "let's clone Notion." Start with "what do people actually do in Notion?" User interviews, workflow analysis, usage patterns. The output is 3-5 core workflows per application instead of bloated feature lists. For Notion, that might be database creation and management, page organization and permissions, collaborative editing. Not Notion AI, not import/export, and not the template gallery. This is the engineering spec before the factory floor moves.
Spec. The PRD scopes to those workflows, nothing else. Every feature in the spec exists because it supports a task. If it doesn't contribute to a learnable, verifiable workflow it's cut. This is where you resist the vanity pull.
Build deep, not wide. Within the scoped workflows, build exhaustively. Every edge case, every state transition, every loading screen, every way a real user would interact with that flow. A model training in this environment should hit the same interaction patterns, decision points, and failure modes it would face on the real Notion for the workflows that matter. Quality lives in depth, not breadth.
Tasks are the quality gate. The environment isn't done when it looks right. It's done when it supports tasks that are difficult (pass@5 < 30%), realistic, reliably verifiable, and hard to hack. If the task designers can't write strong tasks against the environment, the environment goes back to the line. Tasks are QC.
Ship and retool. "Done" means a strong task distribution, not a perfect replica. Once you're there, ship the environment and retool the line for the next one - Figma, Canva, Linear, whatever the labs need.
The only number that matters
Feature coverage measures what an environment has. Task coverage measures what an environment teaches. A 40% feature-complete environment with an airtight task distribution will produce better models than a 95% feature-complete environment with shallow tasks.
There's a floor to fidelity - go too thin and you lose sim-to-real transfer. There's a ceiling to completeness - go too thick and you lose velocity. The factory finds the zone in between by redefining what quality means: does this produce better tasks?
Ford didn't build every car. He built a system for building cars. The tasks are still the product - the factory is how you ship them without choosing between quality and speed.
We obsess over task design at Chakra Labs.
If you're interested in pushing the edges of model capacity with frontier lab researchers, please reach out.
Nirmal Krishnan
Chakra Team
Nirmal Krishnan is a co-founder at Chakra Labs. Prior to starting the company, he spent time in data, markets, and early-stage startups. He studied computer science and machine learning at Johns Hopkins, where he pursued a bachelor’s and master's degree in computational genomics, publishing papers on prostate cancer and induced pluripotent stem cells.
