Verifiers
A library of modular components for creating RL environments and training LLM agents.
Platform
One closed loop. From production to a better model.
PI connects deployment, observability, evaluation and reinforcement learning into a single system — so your model improves every cycle without a team of reward engineers.
FIG. 01 / EFFICIENCY
3.2×
Avg. improvement per cycle
FIG. 02 / VELOCITY
48 h
First improvement deployed
FIG. 03 / SCALE
100B+
Parameters on-stack
FIG. 04 / LABOR
0
Reward functions written
Self-generating eval suites
Auto-instrumented agents
RL across 2,500+ environments
Serverless model inference
Real-time training observability
Production data flywheel
The model is the lab.
The lab never sleeps.
- 01
Deploy
Ship your model to production with one command. Pacer instruments every inference automatically — no SDK changes.
- 02
Observe
The platform tracks where your model hesitates, fails, or gets corrected — and turns every blind spot into a synthetic eval.
- 03
Train
RL post-training runs continuously against your model's own failure surface — not generic benchmarks.
- 04
Improve
Each cycle ships a verified checkpoint back to production, gated by your evals. The loop starts again — automatically.
Environment Hub
Access and contribute to 2,500+ open-source RL environments and a community of researchers and developers.
Request training
MODULE: AUTO-EVALS
Evals that write themselves
No more curating benchmark datasets. Pacer synthesizes targeted evals from your production traces — every blind spot becomes a test case within hours.
MODULE: RL
2,500+ RL environments
The largest open-source RL environment library. Code, science, reasoning, tool use — filtered by what your model actually needs.
MODULE: COMPUTE
H200 to B300
Spot or reserved clusters. Unified across 50+ providers with InfiniBand networking and real-time observability.
MODULE: FLYWHEEL
Production → Signal
Your users are your labelers. Every correction and retry flows back into training automatically — no pipeline to build.
Platform
Everything a lab needs. None of the overhead.
Integration
From zero to self-improving in an afternoon.
Point Pacer at your existing model checkpoint. We instrument your deployment, start observing production, and begin the first training cycle — typically within 48 hours.
First improvement: avg. 48 hours
FAQ
Questions, answered.
Do I need to write reward functions?
No. Pacer derives training signal from production behavior — failures, corrections, retries — and from verifiable outcomes such as passing tests or valid tool calls. Rubrics are synthesized and versioned automatically, and you can review or override any of them.
Which models can I bring?
Any open-weight checkpoint you can serve — from small task-specific models to 100B+ parameter models. Point Pacer at your existing checkpoint; there is no SDK to adopt.
How fast is the first improvement?
Most teams see a first verified checkpoint deployed within 48 hours of instrumenting production traffic.
Where does training run, and who owns the weights?
On on-demand or reserved GPU capacity across 50+ providers, or inside your own compliance boundary. Weights, data and reward specifications remain yours.
What happens if a new checkpoint regresses?
Every candidate is gated by your eval suite before it ships. Checkpoints carry full lineage — environment version, reward spec and evaluation results — so rollbacks are a single command.
[ Ready? ]
Your model should get smarter while you sleep.
Engineers, at full stride. Point PI at your checkpoint — first improvement in about 48 hours.