docs: fill CLI reference gaps for data prep and rollout collection

fern/versions/latest/pages/get-started/installation.mdx

@@ -8,9 +8,25 @@ position: 2
 Python 3.12 is required. Refer to [Prerequisites](/get-started/prerequisites) for full system requirements.
 </Info>
 
-Clone from git to get the latest features and environments. If you intend to use NeMo Gym with NeMo RL, use the latest NGC container.
+Install from PyPI for the quickest setup. Clone from git if you need the latest features and environments, or use the NGC container if you intend to use NeMo Gym with NeMo RL.
 
 <Tabs>
+<Tab title="PyPI">
+
+```bash
+pip install nemo-gym
+```
+
+Or with [uv](https://docs.astral.sh/uv/):
+
+```bash
+uv venv --python 3.12 && source .venv/bin/activate
+uv pip install nemo-gym
+```
+
+The package includes built-in environments and CLI commands. Config and data paths resolve against the package install location automatically. To pin a release: `pip install nemo-gym==0.3.1`.
+
+</Tab>
 <Tab title="Git">
 
 ```bash

fern/versions/latest/pages/reference/cli-commands.mdx

@@ -130,7 +130,8 @@ Perform a batch of rollout collection.
 | `num_repeats` | Optional[int] | The number of times to repeat each example to run. Useful if you want to calculate mean@k, such as mean@4 or mean@16. |
 | `num_repeats_add_seed` | bool | When num_repeats >1, add a "seed" parameter on the Responses create params. |
 | `num_samples_in_parallel` | Optional[int] | Limit the number of concurrent samples running at once. |
-| `responses_create_params` | Dict | Overrides for the `responses_create_params`, such as `temperature` and `max_output_tokens`. |
+| `responses_create_params` | Dict | Overrides for the `responses_create_params`, such as `temperature` and `max_output_tokens`. Refer to [Generation Parameters](#generation-parameters). |
+| `resume_from_cache` | bool | Resume an interrupted run by skipping rows already completed. Default: `False`. Refer to [Resume Interrupted Runs](#resume-interrupted-runs). |
 
 **Example**
 
@@ -144,6 +145,37 @@ ng_collect_rollouts \
     +num_samples_in_parallel=10
 ```
 
+#### Generation Parameters
+
+Sampling parameters such as `temperature`, `max_output_tokens`, and `top_p` are not standalone CLI flags — they are passed as overrides inside `responses_create_params` using Hydra's nested dot syntax. Overrides are merged into each input row's existing `responses_create_params` with a **shallow** merge (top-level keys only):
+
+```bash
+ng_collect_rollouts \
+    +agent_name=example_single_tool_call_simple_agent \
+    +input_jsonl_fpath=weather_query.jsonl \
+    +output_jsonl_fpath=weather_rollouts.jsonl \
+    ++responses_create_params.temperature=1.0 \
+    ++responses_create_params.top_p=1.0 \
+    ++responses_create_params.max_output_tokens=4096
+```
+
+The same syntax works for `ng_e2e_collect_rollouts`. Top-level fields such as `temperature` and `max_output_tokens` are straightforward. For nested objects (for example, `++responses_create_params.reasoning.effort=low`), the entire nested dict replaces the row's existing value at that key — other fields under the same nested object are not preserved.
+
+#### Resume Interrupted Runs
+
+Setting `+resume_from_cache=true` lets you restart the **same command** after a crash or interruption and pick up only the rows that have not finished yet. It works for both `ng_collect_rollouts` and `ng_e2e_collect_rollouts`, across any environment.
+
+How it works:
+
+- **Materialized inputs.** On the first run, the fully expanded input rows (after `num_repeats`, `limit`, `prompt_config`, and any overrides) are written to a sidecar file next to your output. The path is derived from `output_jsonl_fpath` by appending `_materialized_inputs` to the stem — so `rollouts.jsonl` produces `rollouts_materialized_inputs.jsonl`.
+- **Incremental output.** Results are flushed to `output_jsonl_fpath` after each completed rollout, so partial output survives a crash.
+- **Matching.** On resume, completed work is matched by `(task_index, rollout_index)` against the materialized inputs, and already-completed rows are skipped. The run prints a summary such as the number of original input rows, rows already done, and rows that still need to be run.
+- **Fallback.** If either the materialized inputs or the output file is missing, resume is skipped and the run starts fresh. With the default `resume_from_cache=False`, existing output is cleared before the run.
+
+<Warning>
+If you change the config, schema, or data between runs, the materialized inputs become stale and resume will diff against the old expansion. Delete the `*_materialized_inputs.jsonl` file (and the output file) to start fresh.
+</Warning>
+
 ### `ng_e2e_collect_rollouts` / `nemo_gym_e2e_collect_rollouts`
 
 Spin up all necessary servers and perform a batch of rollout collection using each dataset inside the provided configs.
@@ -154,7 +186,8 @@ Spin up all necessary servers and perform a batch of rollout collection using ea
 | --- | --- | --- |
 | `output_jsonl_fpath` | str | The output data JSONL file path. |
 | `num_samples_in_parallel` | Optional[int] | Limit the number of concurrent samples running at once. |
-| `responses_create_params` | Dict | Overrides for the `responses_create_params`, such as `temperature` and `max_output_tokens`. |
+| `responses_create_params` | Dict | Overrides for the `responses_create_params`, such as `temperature` and `max_output_tokens`. Refer to [Generation Parameters](#generation-parameters). |
+| `resume_from_cache` | bool | Resume an interrupted run by skipping rows already completed. Default: `False`. Refer to [Resume Interrupted Runs](#resume-interrupted-runs). |
 
 **Examples**
 
@@ -263,6 +296,38 @@ ng_prepare_data "+config_paths=[${config_paths}]" \
 
 ---
 
+### `ng_materialize_prompts` / `nemo_gym_materialize_prompts`
+
+Apply a prompt template to raw JSONL data, producing materialized JSONL with populated `responses_create_params.input` for RL training.
+
+Each input row must **not** already have a populated `responses_create_params.input`; the command applies the prompt template from `prompt_config` to each row, fills in the input, and preserves the row's other fields.
+
+**Parameters**
+
+| Parameter | Type | Description |
+| --- | --- | --- |
+| `input_jsonl_fpath` | str | Raw JSONL data (rows without `responses_create_params.input`). |
+| `prompt_config` | str | Path to the prompt YAML file to apply. |
+| `output_jsonl_fpath` | str | Output path for the materialized JSONL with populated prompts. |
+
+**Example**
+
+```bash
+ng_materialize_prompts \
+    +input_jsonl_fpath=data/my_dataset.jsonl \
+    +prompt_config=/path/to/my_prompt.yaml \
+    +output_jsonl_fpath=my_dataset_materialized.jsonl
+```
+
+<Note>
+**Which data-preparation command should I use?**
+
+- **`ng_materialize_prompts`** — a focused, standalone step that applies a prompt template to raw rows to populate `responses_create_params.input`. No servers are started. Use it when you have raw data and just need to turn it into prompt-ready rows.
+- **`ng_prepare_data`** — the full preparation pipeline for training: it can download missing datasets, validate data, and compute dataset metrics, writing train/validation splits and metrics artifacts. Use it to prepare and validate datasets for training or PR submission.
+</Note>
+
+---
+
 ## Dataset Registry - GitLab
 
 Commands for uploading, downloading, and managing datasets in GitLab Model Registry.

fern/versions/v0.3.0/pages/get-started/installation.mdx

@@ -8,9 +8,25 @@ position: 2
 Python 3.12 is required. Refer to [Prerequisites](/get-started/prerequisites) for full system requirements.
 </Info>
 
-Clone from git to get the latest features and environments. If you intend to use NeMo Gym with NeMo RL, use the latest NGC container.
+Install from PyPI for the quickest setup. Clone from git if you need the latest features and environments, or use the NGC container if you intend to use NeMo Gym with NeMo RL.
 
 <Tabs>
+<Tab title="PyPI">
+
+```bash
+pip install nemo-gym
+```
+
+Or with [uv](https://docs.astral.sh/uv/):
+
+```bash
+uv venv --python 3.12 && source .venv/bin/activate
+uv pip install nemo-gym
+```
+
+The package includes built-in environments and CLI commands. Config and data paths resolve against the package install location automatically. To pin a release: `pip install nemo-gym==0.3.1`.
+
+</Tab>
 <Tab title="Git">
 
 ```bash

fern/versions/v0.3.0/pages/reference/cli-commands.mdx

@@ -130,7 +130,8 @@ Perform a batch of rollout collection.
 | `num_repeats` | Optional[int] | The number of times to repeat each example to run. Useful if you want to calculate mean@k, such as mean@4 or mean@16. |
 | `num_repeats_add_seed` | bool | When num_repeats >1, add a "seed" parameter on the Responses create params. |
 | `num_samples_in_parallel` | Optional[int] | Limit the number of concurrent samples running at once. |
-| `responses_create_params` | Dict | Overrides for the `responses_create_params`, such as `temperature` and `max_output_tokens`. |
+| `responses_create_params` | Dict | Overrides for the `responses_create_params`, such as `temperature` and `max_output_tokens`. Refer to [Generation Parameters](#generation-parameters). |
+| `resume_from_cache` | bool | Resume an interrupted run by skipping rows already completed. Default: `False`. Refer to [Resume Interrupted Runs](#resume-interrupted-runs). |
 
 **Example**
 
@@ -144,6 +145,37 @@ ng_collect_rollouts \
     +num_samples_in_parallel=10
 ```
 
+#### Generation Parameters
+
+Sampling parameters such as `temperature`, `max_output_tokens`, and `top_p` are not standalone CLI flags — they are passed as overrides inside `responses_create_params` using Hydra's nested dot syntax. Overrides are merged into each input row's existing `responses_create_params` with a **shallow** merge (top-level keys only):
+
+```bash
+ng_collect_rollouts \
+    +agent_name=example_single_tool_call_simple_agent \
+    +input_jsonl_fpath=weather_query.jsonl \
+    +output_jsonl_fpath=weather_rollouts.jsonl \
+    ++responses_create_params.temperature=1.0 \
+    ++responses_create_params.top_p=1.0 \
+    ++responses_create_params.max_output_tokens=4096
+```
+
+The same syntax works for `ng_e2e_collect_rollouts`. Top-level fields such as `temperature` and `max_output_tokens` are straightforward. For nested objects (for example, `++responses_create_params.reasoning.effort=low`), the entire nested dict replaces the row's existing value at that key — other fields under the same nested object are not preserved.
+
+#### Resume Interrupted Runs
+
+Setting `+resume_from_cache=true` lets you restart the **same command** after a crash or interruption and pick up only the rows that have not finished yet. It works for both `ng_collect_rollouts` and `ng_e2e_collect_rollouts`, across any environment.
+
+How it works:
+
+- **Materialized inputs.** On the first run, the fully expanded input rows (after `num_repeats`, `limit`, `prompt_config`, and any overrides) are written to a sidecar file next to your output. The path is derived from `output_jsonl_fpath` by appending `_materialized_inputs` to the stem — so `rollouts.jsonl` produces `rollouts_materialized_inputs.jsonl`.
+- **Incremental output.** Results are flushed to `output_jsonl_fpath` after each completed rollout, so partial output survives a crash.
+- **Matching.** On resume, completed work is matched by `(task_index, rollout_index)` against the materialized inputs, and already-completed rows are skipped. The run prints a summary such as the number of original input rows, rows already done, and rows that still need to be run.
+- **Fallback.** If either the materialized inputs or the output file is missing, resume is skipped and the run starts fresh. With the default `resume_from_cache=False`, existing output is cleared before the run.
+
+<Warning>
+If you change the config, schema, or data between runs, the materialized inputs become stale and resume will diff against the old expansion. Delete the `*_materialized_inputs.jsonl` file (and the output file) to start fresh.
+</Warning>
+
 ### `ng_e2e_collect_rollouts` / `nemo_gym_e2e_collect_rollouts`
 
 Spin up all necessary servers and perform a batch of rollout collection using each dataset inside the provided configs.
@@ -154,7 +186,8 @@ Spin up all necessary servers and perform a batch of rollout collection using ea
 | --- | --- | --- |
 | `output_jsonl_fpath` | str | The output data JSONL file path. |
 | `num_samples_in_parallel` | Optional[int] | Limit the number of concurrent samples running at once. |
-| `responses_create_params` | Dict | Overrides for the `responses_create_params`, such as `temperature` and `max_output_tokens`. |
+| `responses_create_params` | Dict | Overrides for the `responses_create_params`, such as `temperature` and `max_output_tokens`. Refer to [Generation Parameters](#generation-parameters). |
+| `resume_from_cache` | bool | Resume an interrupted run by skipping rows already completed. Default: `False`. Refer to [Resume Interrupted Runs](#resume-interrupted-runs). |
 
 **Examples**
 
@@ -263,6 +296,38 @@ ng_prepare_data "+config_paths=[${config_paths}]" \
 
 ---
 
+### `ng_materialize_prompts` / `nemo_gym_materialize_prompts`
+
+Apply a prompt template to raw JSONL data, producing materialized JSONL with populated `responses_create_params.input` for RL training.
+
+Each input row must **not** already have a populated `responses_create_params.input`; the command applies the prompt template from `prompt_config` to each row, fills in the input, and preserves the row's other fields.
+
+**Parameters**
+
+| Parameter | Type | Description |
+| --- | --- | --- |
+| `input_jsonl_fpath` | str | Raw JSONL data (rows without `responses_create_params.input`). |
+| `prompt_config` | str | Path to the prompt YAML file to apply. |
+| `output_jsonl_fpath` | str | Output path for the materialized JSONL with populated prompts. |
+
+**Example**
+
+```bash
+ng_materialize_prompts \
+    +input_jsonl_fpath=data/my_dataset.jsonl \
+    +prompt_config=/path/to/my_prompt.yaml \
+    +output_jsonl_fpath=my_dataset_materialized.jsonl
+```
+
+<Note>
+**Which data-preparation command should I use?**
+
+- **`ng_materialize_prompts`** — a focused, standalone step that applies a prompt template to raw rows to populate `responses_create_params.input`. No servers are started. Use it when you have raw data and just need to turn it into prompt-ready rows.
+- **`ng_prepare_data`** — the full preparation pipeline for training: it can download missing datasets, validate data, and compute dataset metrics, writing train/validation splits and metrics artifacts. Use it to prepare and validate datasets for training or PR submission.
+</Note>
+
+---
+
 ## Dataset Registry - GitLab
 
 Commands for uploading, downloading, and managing datasets in GitLab Model Registry.