Post-training that runs itself — without losing the thread
XYZ AI Lab builds the alignment and post-training stack for frontier language models. We pursue a bounded AI4AI paradigm: let AI drive the loop so the work is less hands-on and more autonomous — while every step stays auditable and every outcome stays tractable.
Bounded AI4AI — four properties held in tension
Automation that can't be inspected is a liability. We treat autonomy and accountability as co-equal constraints: the loop is designed so that gaining one never costs the other.
Autonomous
AI orchestrates data curation, reward modeling, and evaluation. The human sets intent; the system carries the load.
Bounded
Every autonomous action operates inside explicit guardrails — budgets, invariants, and stop conditions that cannot be crossed.
Auditable
Every decision, prompt, and gradient step is logged as an inspectable trajectory. Nothing about the process is a black box.
Tractable
Outcomes are reproducible and attributable. When a model improves — or regresses — we can trace exactly why.
A closed loop where AI improves AI
Post-training becomes a self-driving pipeline. Each stage is automated, but each hands off a signed, replayable artifact to the next — so autonomy compounds without drift.
Elicit
Synthesize and mine prompts that probe the frontier of current capability.
Generate
Sample candidate responses and reasoning traces under controlled decoding.
Judge
AI critics and reward models score against explicit, versioned rubrics.
Optimize
Preference and RL updates applied within bounded trust regions.
Verify
Regression, safety, and audit gates must pass before the loop closes.
Autonomy you can hold accountable
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Explicit invariants
Safety and capability constraints are declared up front and checked continuously — not bolted on after training.
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Replayable trajectories
Any run can be re-executed bit-for-bit from its logged decisions, making every result independently verifiable.
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Hard stop conditions
The loop halts itself the moment a bound is approached, before a violation can occur.
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Attributable outcomes
Every capability shift is traced to the data, reward, and update that caused it.
Where we push
Our work sits at the intersection of autonomous experimentation and the discipline required to trust it.
Self-improving reward models
AI critics that refine their own rubrics while staying anchored to human-declared intent.
Bounded RL from AI feedback
Trust-region preference optimization with provable limits on capability drift per step.
Audit-native training
Training infrastructure where the audit log is the primary artifact, not an afterthought.
Tractable evaluation
Evaluations that attribute every score to a cause, so improvement is explainable.
Build the autonomous, accountable future of post-training
We partner with researchers and teams who believe autonomy and auditability belong together. If that's you, we'd like to talk.