Directory label
ML engineering benchmarks and RL environments
A label other catalogs put on entries, not one of the domains on our map. On the map, each entry here sits in the domain its own site or paper leads with.
Machine learning engineering itself: training and evaluating models, building data pipelines, tuning experiments and debugging the results.
- RL environments
- 7
- AI benchmarks
- 4
- Shares entries with
- 4 categories
Snapshot 2026-11-05. How categories are built · Where the data comes from
In short
- What are RL environments for ML engineering?
- A simulated workplace where an AI agent practises a task (a code repository, a browser, business software) and an automatic grader scores each attempt. Labs train on thousands of these attempts. Bought to improve models. For ML engineering, that means machine learning engineering itself: training and evaluating models, building data pipelines, tuning experiments and debugging the results.
- Who builds RL environments for ML engineering?
- Emulated, Preference Model, dmodel, Vmax, Aviro and EdotEnv, among 7 confirmed below.
- What are the main ML engineering benchmarks?
- SWE, unix-ctf and Long-Horizon Planning.
RL environments 7
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Emulated
Sells RL environments“Each environment provides the agent with a complete, runnable system and the tooling a real engineer would use.” source ↗
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Preference Model
Sells RL environments“That's why we're building RL environments for training capable, better-aligned superintelligences.” source ↗
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dmodel
Sells RL environments“We build RL environments for open-ended interpretability tasks.” source ↗
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Vmax
Sells RL environments“Campaign is our self-play task generation infrastructure. It transforms your proprietary data and evals into new sets of environments” source ↗
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Aviro
Sells RL environments“We turn these worlds into post-training datasets and reinforcement learning environments to train the next frontier of AI.” source ↗
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EdotEnv
Sells RL environments“we build RL environments at scale from financial market data that increase in difficulty, so agents can continuously hillclimb.” source ↗
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Diffuse Labs
Sells RL environments“We build reinforcement learning environments that frontier labs use to train their models.” source ↗
AI benchmarks 4
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SWE
Emulated · built it“We're introducing the **Software Development Automation Benchmark**: 80 tasks that grade not just code generation” source ↗
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unix-ctf
Vmax · built it“We introduce unix-ctf, a procedural generator of capture-the-flag environments for training and evaluating Unix competence in language-model shell agents.” source ↗
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Long-Horizon Planning
No line from its own site or paper places it in ML yet; the catalog lists it here.
1 not yet confirmedListed in the catalog; no line from its own site or paper confirms it yet.
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