RL rollouts
You run the trainer. Grid serves samples on quoted reserved capacity.
/training/rl-rollouts
Keep your trainer.
Scale the rollouts.
Run rollout sampling on Grid while your team owns the trainer and algorithm. Scope a pilot, then quote the reserved capacity.
Quoted capacity · No public rate card · Not self-serve
What to bring to the pilot.
Read the rollout guide- Checkpoint and trainer
- Candidate checkpoint, precision, framework, algorithm and reward or evaluator. Confirm each method and refresh requirement.
- Sampling workload
- Context and output lengths, samples per prompt, concurrency and the useful sample target.
- Capacity window
- Desired region, start, duration and checkpoint-refresh pattern. The quote confirms resources and effective rates.
- Acceptance
- Compare useful samples, quality, checkpoint freshness, throughput and cost against your baseline.
- 01Your checkpoint
- 02Grid sampling
- 03Your trainer
Choose your training path.
Explore training pathsManaged RFT
Grid runs the full loop: data, reward, training and promotion. Access is by invitation.
Capacity and sampling details
Keep the trainer
Your loop stays where it is. Grid is the sampling fleet: OpenAI-compatible completions, sticky sessions for multi-turn trajectories, and a weight-update path we size with you.
Same inference stack
Rollouts run on the factory that already serves Grid. You do not stand up a second engine beside production. Capacity can move between product inference and rollout windows.
Not managed RFT
Managed reinforcement fine-tuning runs the whole loop for you: data, reward, train, promote. RL rollouts are the layer underneath, for teams that already have a trainer and do not want to migrate it.
Quoted capacity
Rollouts run on reserved capacity, not on anonymous Serverless. We start with a proof of concept, then size regions. There is no public rate card.
Questions
Can I keep my own trainer?
Yes. That is the product. Upload checkpoints to object storage we agree on, sample through Grid, and push weight updates on a schedule we set with you.
How is this different from managed RFT?
Managed RFT is the full job: you bring data and a reward, Grid trains and deploys. RL rollouts are sampling and fleet orchestration only. Pick rollouts if the trainer is already yours.
Is this live self-serve?
No. It is reserved capacity, by conversation. Talk to us to size a proof of concept.