What it trains on
Successful tasks that captured both an input and an output, shown as the example count on the Training page. You can train on everything the agent does well, or pick a single training set to distill one kind of work into a specialist. Training needs at least 200 examples; keep sending traces until you cross the floor. Datasets are capped at 1,000 examples; the newest eligible tasks win.Training methods
Three ways to turn the same dataset into an adapter, picked per run. Runs launch from the CLI (belvedir train <project> --method sft|opsd|rl) or automatically (below):
- Supervised fine-tuning teaches the model to reproduce your successful outputs directly (the default and the cheapest).
- On-policy self-distillation lets the model generate its own answers and nudges each one toward what a teacher (the same model shown the known-good output) would have said, so it learns from its own mistakes instead of copying.
- Reinforcement learning generates answers and trains on a score: either an LLM judge grading each sample against your reference outputs, or a cheaper similarity reward. RL runs use GRPO or PPO; leave the algorithm on Auto and Belvedir picks the better fit for your reward and dataset size.
Base models
Small open models that fit a single GPU: Qwen 2.5 7B Instruct, Llama 3.1 8B Instruct, and Qwen 3.5 9B (trained on Tinker). Llama is gated on HuggingFace, so it asks for an HF token; the token is passed to the training sandbox only and never stored.Runs are free right now
Training runs are not billed at the moment: only inference is (routed calls and hosted inference sessions). Runs are capped at 2 hours; typical runs finish well under that.Auto-training
Instead of launching runs by hand, enable auto-training in the Training setup wizard. It triggers a new run either when enough new traces have arrived since the last run or on a fixed cadence, with a cooldown between runs. Auto runs repeat the configuration of your last completed run and are treated exactly like manual ones.What you get back
Method-appropriate metrics recorded on the run (train/eval loss for supervised fine-tuning, distillation loss for self-distillation, mean reward for RL), plus the LoRA adapter itself (a zip of the standard PEFT files). Every run is checked against its own baseline: a model that didn’t beat the plain base model on held-out data (or whose distillation loss or reward regressed) is marked failed instead of ready, with the comparison recorded on the run. Load a ready model on the base withpeft, vLLM’s --lora-modules, or any runtime that speaks LoRA (adapters from the Qwen 3.5 9B base target layers vLLM can’t apply LoRA to, so merge them into the base weights first — the Deploy button and the playground do that for you). Or deploy it without leaving the platform: paste a Together API key on the model’s row and Belvedir ships a merged copy to a scale-to-zero endpoint in your own Together account. See Cloud Inference for the deploy flow and its preconditions.