Separate learning from proof
Training feedback and promotion evidence have different jobs. Held-out tasks remain frozen so iteration cannot silently tune against the final decision set.
EVALUATION + PROMOTION
Reproducible LLM evaluation before production.
Training metrics show that a run changed. Qualification shows whether the resulting skill is correct, robust, and safe enough to promote. g factor binds each decision to a frozen benchmark contract and the exact artifacts being evaluated.
01Frozen held-out tasks
02Base vs. adapted comparison
03Hard promotion gates
04Immutable provenance
WHY G FACTOR
Built for owned, measurable model skills.
Evaluate the real outcome
Use native task results, reopened artifacts, executable tests, and environment receipts instead of relying only on an LLM judge or presentation quality.
Make decisions reproducible
Pin the model, adapter, tokenizer, environment, taskset, reward profile, and benchmark so another run can reconstruct the same comparison.
CAPABILITIES
Evidence for every promotion decision.
Benchmark plans
Define the evaluation contract before inspecting results and preserve it with the run.
- Frozen task and environment revisions
- Declared metrics and hard gates
- Repeatable seeds and execution settings
Matched comparisons
Evaluate the base and adapted policies on the same contract so improvements and regressions are attributable.
- Shared held-out task set
- Correctness, robustness, cost, and latency views
- Explicit failure and uncertainty reporting
Promotion evidence
Keep the decision tied to exact artifacts instead of treating a successful training job as deployment approval.
- Immutable adapter and checkpoint identity
- Environment and verifier receipts
- Reviewable approve-or-reject outcome
BEFORE YOU START
Common questions
How is LLM evaluation different from training reward?
Training reward guides optimization on tasks used during learning. Evaluation measures the resulting model on separate held-out tasks. Higher training reward alone does not show that a model generalizes or is ready for production.
How do you compare a base model with a fine-tuned adapter?
Run both against the same frozen benchmark contract, task set, and execution settings. Compare correctness, robustness, cost, and latency, and retain the exact model and adapter identities so improvements and regressions can be reviewed.
What makes a model qualification reproducible?
The evidence pins the model, adapter, tokenizer, environment, task set, reward profile, and execution settings. Declared metrics and promotion gates remain attached to the artifacts, so a reviewer can reconstruct what was tested and how the decision was made.
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.
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