Allow explicit input files in the all-in-one gym eval run flow

[rystewart-nvidia]

Use cases, pain points, and background

The all-in-one gym eval run flow currently rejects --input and requires a configured dataset split. To run an already-prepared JSONL file, users have to start servers separately with gym env start, then collect rollouts with gym eval run --no-serve --input.

Supporting explicit inputs in the all-in-one flow would simplify running subsets or retrying selected tasks without adding a separate dataset configuration.

Description:

Allow gym eval run --input <prepared-tasks.jsonl> to handle server startup, rollout collection, and shutdown in one command. Any required dataset preparation still happens beforehand.

Design:

• Update nemo_gym/rollout_collection.py to accept an explicit input path without requiring a split.
• Update nemo_gym/cli/eval.py to validate the file before server startup and use it directly, bypassing preparation of configured dataset splits.
• Give explicit input files precedence over configured splits, including when using a custom collection driver.
• Preserve existing split preparation and cache reuse when no input file is supplied.
• Update CLI help in nemo_gym/cli/main.py, the CLI reference under fern/, and the relevant unit tests.

Out of scope:

• Automatically downloading or preparing the dataset supplied through --input.
• Changing dataset formats, agent behavior, or verification logic.
• Changing the existing --no-serve workflow.

Acceptance Criteria:

• An already-prepared input file can be run without --split or --no-serve.
• Missing or invalid file paths fail before servers start.
• Explicit input takes precedence over a configured split.
• Standard collection and custom collection drivers receive the supplied file.
• Existing split preparation and cache reuse still work.
• CLI help and documentation explain that input files must be prepared beforehand.
• Regression tests and a live end-to-end smoke test pass.