The path
1
Decide whether to post-train
Post-training pays off for a stable, well-defined task with a measured gap you can’t close with prompting or retrieval, at enough volume that serving cost or latency matters. See Decide whether to post-train.
2
Define what a good answer is
Write quality rules, freeze a held-out set, and score the models you have today. The same rules select training data and decide what ships. See Evaluate a model.
3
Choose a base model
Start from your constraints: license and publisher, serving latency and cost, and sequence length. Then baseline the candidates on your evaluation. See Choose a base model.
4
Choose SFT or RL
Supervised fine-tuning (SFT) needs answers to imitate. Reinforcement learning (RL) needs a way to score outcomes. See Choose SFT or RL.
5
Prepare data or an environment
For SFT, build conversations with targets you trust. For RL, write tasks, an agent, and a grader. See Prepare data for supervised fine-tuning or Design RL environments and rewards.
6
Run a first job
Start small and confirm the run is learning before you scale. See Fine-tune tool calling with SFT or Train a model to count circles with RL. Both use models available on serverless Loops.
7
Ship it
Check the run’s health, compare the checkpoint with its baseline on held-out data, deploy it, and roll it out behind the model it replaces. See Ship a post-trained model.
What has worked
Each of these published results started from a production task with a measurable definition of quality.
The examples in these docs follow the same patterns on public data: tool calling with SFT, an agent that edits files with RL, and a coding agent with RL.
Where training runs
Loops runs the trainer and sampler on Baseten. Your training script, or the Loops cookbook, can run locally or in a remote instance; see Choose where to run the Loops SDK. Loops picks an availability mode for each run, so you don’t manage infrastructure to get started. Serverless is self-serve and billed for tokens. The dedicated mode covers every supported model and lets you scale a run out with more trainer and sampler replicas. Use Training Jobs instead when you need full-parameter training, a model Loops doesn’t support, or your own training framework in a container. Use RL Rollouts when you own the RL training code and want Baseten’s inference platform to scale its rollouts. Training on Baseten compares all three.Next steps
Decide whether to post-train
Check the task, the gap, the data, and the economics before you train.
Evaluate a model
Define quality and freeze a held-out set before the first run.