> ## Documentation Index > Fetch the complete documentation index at: https://docs.primeintellect.ai/llms.txt > Use this file to discover all available pages before exploring further. # Advanced Configurations > Full configuration reference for Hosted Training runs Hosted Training runs are configured via a `.toml` file. This page covers all available configuration fields, from basic setup to advanced features like multi-environment training, online evaluation, and W\&B integration. ## Full Config Reference Below is a complete annotated config showing all available fields. Required fields are uncommented; optional fields are shown as comments with their defaults. ```toml theme={null} # ============================================================ # Core Configuration (required) # ============================================================ model = "Qwen/Qwen3-30B-A3B-Instruct-2507" # HuggingFace model ID max_steps = 100 # Total training steps batch_size = 256 # Rollouts per training batch rollouts_per_example = 8 # Rollouts generated per dataset example # ============================================================ # Training Hyperparameters (optional) # ============================================================ # learning_rate = 1e-4 # Learning rate for LoRA # lora_alpha = 16 # LoRA alpha scaling factor # oversampling_factor = 2.0 # Oversample factor for rollout generation # trajectory_strategy = "interleaved" # "interleaved" or "branching" # ============================================================ # Secrets (optional) # ============================================================ # env_file = ["secrets.env"] # File(s) containing environment secrets # ============================================================ # Sampling Configuration (required) # ============================================================ [sampling] max_tokens = 512 # Max tokens per model response # enable_thinking = false # Toggle thinking mode (Qwen3.5, Nemotron) # reasoning_effort = "high" # Reasoning effort: "low" | "medium" | "high" (GPT-OSS) # ============================================================ # Environment(s) (at least one required) # ============================================================ [[env]] id = "primeintellect/alphabet-sort" # Environments Hub ID (owner/name) # args = { min_turns = 3, max_turns = 5 } # Arguments passed to load_environment() # Add multiple [[env]] sections for multi-environment training: # [[env]] # id = "primeintellect/another-env" # args = { split = "train", max_examples = 1000 } # ============================================================ # Weights & Biases Logging (optional) # ============================================================ # [wandb] # project = "my-project" # W&B project name # name = "my-run-name" # W&B run name # entity = "my-team" # W&B team/entity # ============================================================ # Online Evaluation (optional) # ============================================================ # [eval] # interval = 100 # Run eval every N training steps # num_examples = -1 # Number of eval examples (-1 = all) # rollouts_per_example = 1 # Rollouts per eval example # skip_first_step = false # Skip the pre-training eval of the base model # # [eval.sampling] # Eval-time sampling overrides # max_tokens = 2048 # Max tokens per eval response # temperature = 0.0 # Eval sampling temperature # enable_thinking = false # Toggle thinking mode at eval time # reasoning_effort = "high" # Reasoning effort at eval time # # [[eval.env]] # Environment-specific eval overrides # id = "primeintellect/eval-env" # args = { split = "test" } # num_examples = 30 # rollouts_per_example = 4 # ============================================================ # Validation During Training (optional) # ============================================================ # [val] # num_examples = 64 # Validation examples per check # rollouts_per_example = 1 # Rollouts per validation example # interval = 5 # Validate every N steps # ============================================================ # Rollout Filters (optional) # ============================================================ # [[pre_batch_filters]] # Applied before rollouts fill a batch slot # type = "zero_advantage" # "gibberish" | "repetition" | "zero_advantage" # enforce = true # Drop flagged rollouts (false = metrics only) # # [[post_batch_filters]] # Applied after a batch is assembled # type = "repetition" # enforce = false # ============================================================ # Warm-Start from Checkpoint (optional) # ============================================================ # checkpoint_id = "..." # Resume training from an existing checkpoint # ============================================================ # Checkpoints (optional) # ============================================================ # [checkpoints] # interval = 100 # Save checkpoint every N steps # keep_cloud = 5 # Keep N checkpoints in cloud (-1 = keep all) # ============================================================ # Adapters (optional) # ============================================================ # [adapters] # interval = 0 # Upload adapter every N steps (0 = only at run end) # keep_last = 3 # Keep N adapters in cloud (-1 = keep all) # ============================================================ # Infrastructure (optional) # ============================================================ # [infrastructure] # compute_size = "M" # CPU allocation: S, M (default), or L ``` ## Field Reference ### Core Fields | Field | Type | Required | Description | | ---------------------- | ------- | -------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `model` | string | ✓ | HuggingFace model ID. Must be a [supported model](/hosted-training/models-and-pricing). Run `prime train models` to see available options. | | `max_steps` | integer | ✓ | Total number of training steps. | | `batch_size` | integer | ✓ | Number of rollouts consumed per training batch. Larger values improve stability. | | `rollouts_per_example` | integer | ✓ | Number of rollouts generated per dataset example. Higher values give more reward signal diversity. | | `checkpoint_id` | string | — | Checkpoint ID to warm-start from. The checkpoint must be in READY status, accessible to you, and from a run using the same model. See [Warm-Starting from a Checkpoint](#warm-starting-from-a-checkpoint). | ### Training Hyperparameters | Field | Type | Default | Description | | --------------------- | ---------------- | --------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `learning_rate` | float | `1e-4` | Learning rate for the LoRA adapter. | | `lora_alpha` | integer | `16` | LoRA alpha scaling factor. Controls the magnitude of LoRA updates. | | `oversampling_factor` | float | `2.0` | Generate this many more rollouts than needed per batch to ensure sufficient data. | | `trajectory_strategy` | string | `"interleaved"` | How multi-turn trajectories are generated. `"interleaved"` runs turns across examples concurrently. `"branching"` generates full trajectories per example before moving on. | | `env_file` | array of strings | `[]` | Path(s) to `.env` files containing secrets (e.g., API keys). See [Secrets Management](#secrets-management). | ### Sampling | Field | Type | Required | Description | | ----------------------------- | ------- | -------- | ----------------------------------------------------------------------------------------------------------------------- | | `[sampling].max_tokens` | integer | ✓ | Maximum number of tokens the model can generate per response turn. | | `[sampling].enable_thinking` | boolean | — | Toggle thinking mode for supported models. Mutually exclusive with `reasoning_effort`. | | `[sampling].reasoning_effort` | string | — | Reasoning effort for supported models. One of `"low"`, `"medium"`, `"high"`. Mutually exclusive with `enable_thinking`. | ### Eval Sampling Overrides the inference server's default sampling for eval-time rollouts only. All fields are optional; when the whole `[eval.sampling]` block is omitted, eval uses the server defaults. | Field | Type | Required | Description | | ---------------------------------- | ------- | -------- | ------------------------------------------------------------------------------------------------------------------------------------ | | `[eval.sampling].max_tokens` | integer | — | Maximum tokens generated per eval response turn. | | `[eval.sampling].temperature` | float | — | Eval sampling temperature. `0.0` for deterministic eval scoring. | | `[eval.sampling].extra_body` | table | — | Free-form extra parameters forwarded with each eval request to the inference server. | | `[eval.sampling].enable_thinking` | boolean | — | Toggle thinking mode at eval time for supported models. Mutually exclusive with `reasoning_effort`. | | `[eval.sampling].reasoning_effort` | string | — | Reasoning effort at eval time for supported models. One of `"low"`, `"medium"`, `"high"`. Mutually exclusive with `enable_thinking`. | ### Environment | Field | Type | Required | Description | | -------------- | ------ | -------- | -------------------------------------------------------------------- | | `[[env]].id` | string | ✓ | Environment ID on the Environments Hub, in `owner/name` format. | | `[[env]].args` | table | — | Arguments passed to the environment's `load_environment()` function. | ## Multi-Environment Training You can train on multiple environments simultaneously by adding multiple `[[env]]` sections: ```toml theme={null} [[env]] id = "primeintellect/alphabet-sort" args = { min_turns = 3, max_turns = 5 } [[env]] id = "primeintellect/gsm8k" args = { split = "train" } ``` ## Online Evaluation Enable periodic evaluation during training to track progress without interrupting the run: ```toml theme={null} [eval] interval = 100 # Evaluate every 100 steps num_examples = -1 # Use all eval examples rollouts_per_example = 1 skip_first_step = false # Evaluate the base model before training starts [[eval.env]] id = "primeintellect/alphabet-sort" args = { split = "test" } num_examples = 50 rollouts_per_example = 4 ``` The `[eval]` section sets global defaults, and `[[eval.env]]` sections can override settings per environment. ### Eval Sampling Eval rollouts use the inference server's default sampling unless overridden via `[eval.sampling]`. The fields mirror `[sampling]` and `[teacher.sampling]` so the same knobs work everywhere — most commonly, you'd turn thinking off at eval time to get deterministic, faster scoring on a model that uses chain-of-thought during training: ```toml theme={null} [eval.sampling] max_tokens = 2048 temperature = 0.0 enable_thinking = false # Disable thinking at eval time # reasoning_effort = "high" # Or constrain reasoning effort ``` `enable_thinking` and `reasoning_effort` are mutually exclusive — set at most one. Both ride on `extra_body.chat_template_kwargs` under the hood; you can also set `extra_body` directly if you need other chat-template controls. ## Validation Validation is a lightweight check that runs more frequently than full evaluation: ```toml theme={null} [val] num_examples = 64 rollouts_per_example = 1 interval = 5 # Validate every 5 steps ``` This uses the training environment's validation split (if available) and reports metrics to W\&B and the dashboard. ## Rollout Filters prime-rl filters rollouts at two points in the training pipeline. `[[pre_batch_filters]]` run before a rollout enters the training batch, so flagged rollouts never consume a batch slot; `[[post_batch_filters]]` run after a batch is assembled, and flagged rollouts are recorded but not shipped to the trainer. Three filter types are available — `gibberish`, `repetition`, and `zero_advantage` — and each either records detection metrics only (`enforce = false`) or drops flagged rollouts (`enforce = true`). By default, all three filters run in monitor mode pre-batch and `zero_advantage` is enforced post-batch. Setting either section replaces the default filter list for that slot. To focus training compute on examples with useful reward signal — the successor to the removed difficulty buffer's `online_difficulty_filtering` — enforce the zero-advantage filter pre-batch: ```toml theme={null} [[pre_batch_filters]] type = "zero_advantage" enforce = true ``` Type-specific tuning knobs (such as `repetition`'s `window` and `prob_threshold`) pass through to the trainer as written. ## Checkpoints Control how often checkpoints are saved and how many are retained in cloud storage: ```toml theme={null} [checkpoints] interval = 100 # Save checkpoint every 100 steps keep_cloud = 5 # Keep last 5 checkpoints in cloud ``` | Field | Type | Default | Description | | ------------ | ------- | --------------- | -------------------------------------------------------------------------------------- | | `interval` | integer | cluster default | Save a checkpoint every N training steps. | | `keep_cloud` | integer | `5` | Number of checkpoints to retain in cloud storage. Set to `-1` to keep all checkpoints. | Checkpoints enable resuming training from a specific step if a run is interrupted. They're automatically uploaded to cloud storage and can be used to create new runs from a saved state. ## Warm-Starting from a Checkpoint Start a new run from an existing checkpoint by setting `checkpoint_id` at the top level of your config. The checkpoint must be READY, use the same model, and you need access to the original run. ```toml theme={null} checkpoint_id = "cp_abc123" ``` List available checkpoints with `prime train checkpoints `. ## Adapters Configure periodic adapter uploads during training. Adapters are LoRA weights that can be deployed for inference. ```toml theme={null} [adapters] interval = 100 # Upload adapter every 100 steps keep_last = 3 # Keep last 3 adapters in cloud ``` | Field | Type | Default | Description | | ----------- | ------- | ------- | ---------------------------------------------------------------------------------------------- | | `interval` | integer | `0` | Upload adapter every N training steps. Set to `0` to only upload the final adapter at run end. | | `keep_last` | integer | `3` | Number of adapters to retain in cloud storage. Set to `-1` to keep all adapters. | Deployed adapters are protected from automatic cleanup. If you deploy an adapter for inference, it will not be deleted even if it exceeds the `keep_last` limit. ## Infrastructure Control the CPU and memory resources allocated to your environment containers. This only affects the environments you provide — trainer and inference infrastructure is fully managed by us. ```toml theme={null} [infrastructure] compute_size = "L" ``` | Size | Description | | ---- | ---------------------------------------------------------------------------------------------------------------------- | | `S` | Lower CPU allocation. Suitable for lightweight environments. | | `M` | Default. Balanced allocation for most workloads. | | `L` | High CPU allocation. Use for environments that compile code or for vision-language models with heavy image processing. | If not specified, runs default to `M`. Most users won't need to change this — use `L` if you notice slow CPU-bound operations during training. ## Tailscale Networking Tailscale networking is an **enterprise-only** feature. Contact your account team to enable it on your organization. When enabled, every env-server (training and eval) for the run joins your Tailscale tailnet via a sidecar. From inside your environment code you can then reach private services — internal APIs, MCP servers, datasets behind a VPN — by their Tailscale IP, MagicDNS hostname, or by native LAN IP if a [subnet router](https://tailscale.com/kb/1019/subnets) advertises it. ```toml theme={null} [tailscale] enabled = true # auth_key = "tskey-auth-..." # preferably via TAILSCALE_AUTH_KEY env var # hostname_prefix = "prime-hosted-training" ``` | Field | Type | Default | Description | | ----------------------------- | ------- | ------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `[tailscale].enabled` | boolean | `false` | Toggle the per-run sidecar. | | `[tailscale].auth_key` | string | — | Tailscale [pre-authenticated key](https://tailscale.com/kb/1085/auth-keys) (must start with `tskey-auth-`). OAuth client secrets are not supported. Prefer the `TAILSCALE_AUTH_KEY` environment variable so the secret is not committed to `rl.toml`. | | `[tailscale].hostname_prefix` | string | `"prime-hosted-training"` | Prefix for the Tailscale node name. The full name is derived as `{prefix}-env-{idx}-{run_id}`. 1–30 lowercase alphanumeric chars or hyphens, must start with a letter. | Use a **tagged**, ephemeral, reusable auth key. Tagged keys let you scope the env-servers in your tailnet ACL without granting them the same access as a user-owned device. ## Weights & Biases Integration Log training metrics, reward curves, and rollout samples to W\&B: ```toml theme={null} [wandb] project = "my-rl-experiments" name = "qwen3-30b-alphabet-sort" entity = "my-team" ``` When W\&B is configured, all training metrics, evaluation results, and sample rollouts are logged automatically. ## Secrets Management The recommended way to supply secrets to Hosted Training is via [environment secrets](/tutorials-environments/secrets). Secrets linked or added to your environment are automatically injected at runtime — no config changes needed. If you prefer to supply secrets via a file, you can use `env_file` in your training config instead: ```toml theme={null} env_file = ["secrets.env"] ``` The `secrets.env` file should contain key-value pairs: ``` OPENAI_API_KEY=sk-... CUSTOM_API_KEY=... ``` You can also manage secrets via the CLI: ```bash theme={null} prime secret list # list global secrets prime env secret list my-env # list secrets for an environment ``` In your environment code, validate required keys early using `vf.ensure_keys()`: ```python theme={null} def load_environment(api_key_var: str = "OPENAI_API_KEY") -> vf.Environment: vf.ensure_keys([api_key_var]) # ... ``` Walk through a complete training run step by step. Solutions for common issues with Hosted Training.