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name: data-and-rewards description: "Prepare and validate verl post-training parquet data, reward-model metadata, and custom reward functions." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Data and Rewards

Use this sub-skill when the user needs to prepare, inspect, or debug verl post-training datasets and rewards before launching PPO-style training.

Route Here For

• Creating or reviewing parquet rows with data_source, chat-template prompt, ability, reward_model, and extra_info fields.
• Validating one parquet file before wiring it into data.train_files or data.val_files.
• Aligning reward_model.ground_truth extraction with built-in or custom reward functions.
• Setting custom_reward_function.path and custom_reward_function.name for a Python reward module.
• Debugging prompt schema, multimodal placeholders, missing reward metadata, or reward-manager score failures.

Bundled References

• Read references/data-formats.md for parquet row schemas, chat prompts, multimodal columns, and dataset-loader behavior.
• Read references/reward-functions.md for built-in reward dispatch, custom reward signatures, return values, and config keys.
• Read references/troubleshooting.md for common schema/reward failures and targeted fixes.

Bundled Script

• Run python scripts/validate_verl_parquet.py --help for usage.
• Run python scripts/validate_verl_parquet.py path/to/train.parquet --style-rule-requires-ground-truth --max-rows 200 to inspect schema without importing verl.
• Use --json-output report.json when a future agent needs machine-readable validation results.

Working Pattern

1. Confirm every row has a dataset identifier in data_source and chat messages under the configured prompt key, usually prompt.
2. Confirm rule-based rows carry reward_model.style == "rule" and reward_model.ground_truth in the exact format expected by the reward function.
3. Keep reproducibility metadata such as split, index, raw question, raw answer, tool settings, and interaction settings under extra_info.
4. Validate a small sample with the bundled script before changing training configs.
5. For custom rewards, make the reward module importable by path and expose either compute_score or the configured function name.

Boundaries

• This sub-skill does not own full PPO launch commands, rollout engine selection, installation, or cluster setup.
• Treat repo examples as schema evidence only; do not require future agents to call network-backed preprocessing scripts at runtime.