> ## Documentation Index
> Fetch the complete documentation index at: https://docs.veri.studio/llms.txt
> Use this file to discover all available pages before exploring further.

# veri rewards + veri jobs

> Upload reward functions, submit training jobs from configs or inline flags, and manage them through completion.

Training a model on Veri is a two-step CLI flow: register a reward function with `veri rewards upload`, then submit a job with `veri jobs create` (or `veri run`). Once submitted, manage the job through completion with `list / get / cancel / download`.

<Tip>
  For a script-driven flow, you can also use `veri train <file.py>` — it reads a `@training_job(...)`-decorated function from the file, bundles the reward, resolves the dataset, and submits the job in one call. See [`veri run`](/cli/run) for the full decorator + TOML reference.
</Tip>

<Note>
  `veri rewards` does not have a server-side DELETE today. The CLI's `delete` subcommand removes the reward from your local config, not from the control plane. Reward functions are content-addressed, so re-uploading the same file returns the same id.
</Note>

## Reward functions

| Command            | Purpose                                               |
| ------------------ | ----------------------------------------------------- |
| `upload <path.py>` | Register a reward function. Auto-runs AST validation. |
| `list`             | List registered reward functions.                     |
| `get <id>`         | Show one reward function.                             |
| `delete <id>`      | Delete a reward function.                             |

### Upload

```bash theme={null}
veri rewards upload ./reward.py --name math-reward
```

`upload` runs an AST-only structural check before uploading:

* File parses as valid Python.
* The file defines at least one function.

The check deliberately stops there — verifying specific signatures (TRL vs DPO vs custom) requires actually importing user code, which the CLI refuses to do in its own process. If your reward function imports modules missing from the training runtime, the failure surfaces in the training logs once the job starts.

Skip validation explicitly when needed:

```bash theme={null}
veri rewards upload ./reward.py --skip-validation
```

| Flag                | Purpose                                |
| ------------------- | -------------------------------------- |
| `--name`            | Display name (default: filename stem). |
| `--format`          | Reward format (`trl` by default).      |
| `--skip-validation` | Don't run the AST check.               |

### List, get, delete

```bash theme={null}
veri rewards list
veri rewards get rf_abc
veri rewards delete rf_abc
```

<Note>
  `veri rewards test` (run the reward against a dataset) is not in Phase-1. It depends on dataset row iteration, which is owed by the SDK first.
</Note>

## Training jobs

| Command                     | Purpose                            |
| --------------------------- | ---------------------------------- |
| `create <config>` or inline | Submit a training job.             |
| `list`                      | List jobs.                         |
| `get <id>`                  | Show one job.                      |
| `cancel <id>`               | Cancel a running job.              |
| `download <id>`             | Download the completed checkpoint. |

### Submit from a config

```bash theme={null}
veri jobs create configs/train.toml
# or, via the hero verb:
veri run configs/train.toml
```

The config must declare `kind = "train"`. See [`veri run`](/cli/run#configs-train-toml) for the full schema.

`create` returns the job ID. Pipe-friendly form:

```bash theme={null}
JOB_ID=$(veri jobs create configs/train.toml --quiet)
```

### Submit inline (no config file)

```bash theme={null}
veri jobs create \
  --base-model "Qwen/Qwen3-4B" \
  --dataset ds_abc \
  --reward rf_abc \
  --gpu-type A100-80GB \
  --gpu-count 1
```

Inline form requires `--base-model` AND `--dataset`. Pass `--reward` for `grpo`; omit for `sft_video_gen`.

### Override config values

```bash theme={null}
veri jobs create configs/train.toml \
  --set model.base=Qwen/Qwen3-8B \
  --set method.learning_rate=2e-6 \
  --set resources.gpu_count=4
```

The convenience flags `--base-model`, `--gpu-type`, `--gpu-count`, `--dataset`, `--reward` expand to `--set` internally. See [Override syntax](/cli/run#override-toml-fields-from-the-cli) for type coercion and conflict detection.

### List, get, cancel

```bash theme={null}
veri jobs list                              # most recent 20
veri jobs list --status running             # filter
veri jobs list --method grpo
veri jobs get job_abc
veri jobs cancel job_abc
```

`list` supports `--limit`, `--after`, `--status`, `--method`. All commands accept `--format json`.

### Watch progress

There is no `--follow` log tailer in Phase-1 (deferred until the SDK exposes streaming). For now:

```bash theme={null}
# Poll status in a script:
while [ "$(veri jobs get $JOB_ID --format json | jq -r .status)" = "running" ]; do
  sleep 30
done

# Or open the dashboard for live logs:
veri jobs get $JOB_ID --format json | jq -r .dashboard_url | xargs open
```

### Download a completed checkpoint

```bash theme={null}
veri jobs download job_abc --output-dir ./checkpoints
```

The download URL is only populated after the job reaches `completed`. Trying to download an in-flight job exits `1` with a clear message.

## Exit codes

| Code | Meaning                                                      |
| ---- | ------------------------------------------------------------ |
| `0`  | Success.                                                     |
| `1`  | User error (missing flag, bad config, job not downloadable). |
| `2`  | API error.                                                   |
| `4`  | Auth error.                                                  |

## What's next

<CardGroup cols={3}>
  <Card title="Deploy this model" icon="cloud" href="/cli/deployments">
    Serve the checkpoint with an OpenAI-compatible endpoint.
  </Card>

  <Card title="Evaluate it" icon="chart-line" href="/cli/evals">
    Score it on a held-out dataset.
  </Card>

  <Card title="The run verb" icon="play" href="/cli/run">
    Config schemas and `--set` overrides.
  </Card>
</CardGroup>