Verifiers
A library of modular components for creating RL environments and training LLM agents.
Your model. Trains itself. Ships better.
Open infrastructure for self-improving agents
Request trainingPI is the closed-loop AI stack where models observe their own failures, generate new training signal, and improve without you writing a single reward function.
Deploying with
At industry standards
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
Environment Hub
Access and contribute to 2,500+ open-source RL environments and a community of researchers and developers.
Request training
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.
PI is the closed-loop AI stack where models observe their own failures, generate new training signal, and improve without you writing a single reward function.
Our team worked with
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.
[ What teams say ]
"We used to spend two weeks curating evals. PI generates better evals from failures in two hours. We just don't think about reward engineering anymore."
2 wks → 2 hEval curation time
Compute. Find reliable compute operated globally from a single GPU to largest clusters.
On demand
Instant access to 1-256 GPUs. Use your GPUs across clouds in a single platform.
- 1.1
SLURM, K8s Orchestration
Orchestrate dynamic workloads with enterprise-grade scheduling and container automation.
- 1.2
Infiniband Networking
Scale distributed training with high-bandwidth interconnects across nodes.
- 1.3
Grafana Monitoring Dashboards
Visualize metrics in real time with customizable dashboards for full system observability.
Liquid Reserved Clusters
Request large-scale clusters from 50+ providers. Sell-back idle GPUs to our spot market.
- 2.1
Get quotes from 50+ datacenters within 24 hours
One request, parallel bids for options, from H100, H200, to B200, B300, GB300 NVL72
- 2.2
Re-sell idle GPUs back to our spot market
Resell idle nodes on our spot market with no manual ops. Reclaim capacity instantly when you need it.
- 2.3
Direct assistance from our research and infra engineering team
Dedicated solutions engineer from cluster bring-up through steady-state
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
[ Ready? ]
Your model should get smarter while you sleep.
Engineers, at full stride. Point PI at your checkpoint — first improvement in about 48 hours.