State, not static prompts
Each episode can inspect, mutate, recover, and terminate against a fresh environment snapshot rather than answer a one-shot benchmark question.
COMMERCIAL OPENENV MARKETPLACE
Private AI training environments for real workflows.
Private training environments turn an engineering workflow into an executable learning loop. Agents act on fresh state, use domain-native tools, and must satisfy hidden correctness, safety, and performance checks before quality rewards count.
01Typed stateful actions
02Hidden deterministic checks
03Private graders and evidence
04Bring your own OpenEnv API
WHY G FACTOR
Built for owned, measurable model skills.
Authoritative outcomes
Native artifacts, simulations, tests, and hidden constraints determine success. The agent cannot earn a passing result by merely describing the right answer.
Keep proprietary logic private
Run state, graders, task fixtures, and evidence inside a private environment while exposing only the typed contract required for training.
CAPABILITIES
Specialized environments backed by executable verifiers.
Kernel / Triton optimization
Optimize PyTorch operators with Triton or CUDA while hidden numerical cases and stable GPU timing gate the speed reward.
- Numerical equivalence across hidden shapes
- Stable performance measurement
- Correctness before speed reward
Digital design and formal verification
Train agents to implement synthesizable Verilog RTL and prove held-out Lean 4 theorems against real compiler, simulation, formal, timing, and axiom checks.
- Icarus, Verilator, Yosys/SKY130, and formal equivalence
- Lean 4 elaboration in frozen mathlib context
- Hidden cases and machine-audited correctness
Physics and silicon engineering
Optimize coupled Modelica systems, OpenFOAM/SU2 airfoils, and SKY130 analog circuits against hidden operating conditions and native simulation evidence.
- Solver convergence and conservation balances
- CFD mesh, residual, lift, and drag checks
- PVT, noise, DRC/LVS/PEX, and post-layout replay
CAD, manufacturing, and 3D workflows
Create mechanical solids, FreeCAD CAM plans, Blender scenes, and structured DXF drawings that are evaluated as native artifacts rather than screenshots.
- CadQuery/OpenCascade and STEP/STL geometry
- Collision-safe CNC operations and reparsed G-code
- Blender scene state and entity-level DXF checks
Quantitative finance research
Train agents to express original cross-sectional alpha hypotheses in a restricted formula DSL and improve them against frozen out-of-sample evidence.
- Purged cross-validation and next-period rank IC
- Trading-cost, turnover, and backtest-overfit controls
- Novelty, complexity, and adversarial regime stress
Game and rendering engineering
Write portable GLSL 330 shaders that generalize across hidden scenes and stay inside a measured frame-time budget.
- Compiler and image-error feedback
- Hidden uniforms, transforms, and lighting
- Hard 16 ms frame gate
BEFORE YOU START
Common questions
How is a training environment different from a dataset?
A dataset supplies examples. A training environment lets an agent take actions, inspect changing state, and receive feedback from the task itself. Compilers, simulations, artifact checks, and hidden cases can determine whether a workflow actually succeeded.
Can I connect my own OpenEnv environment?
g factor supports connecting an OpenEnv API for your workflow. The environment exposes typed actions, observations, rewards, and episode completion while proprietary fixtures, graders, and evidence can remain within your private environment.
Which engineering workflows do the environments cover?
The catalog includes GPU kernel optimization, Verilog RTL, Modelica, Lean theorem proving, CFD, CAD and CAM, Blender, analog IC design, quantitative finance research, and rendering. Each environment uses domain-specific tools and checks; discuss the required workflow and access during a demo.
PRIVATE BETA
Turn your workflow into a skill your model can own.
Bring a model, a workflow, or an evaluation problem. We'll map it to a focused post-training and qualification plan.
Request Demo