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name: data-and-rewards description: "Prepare EasyR1 dataset rows, prompt templates, and AutoRewardManager-compatible reward functions." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

data-and-rewards

Use this sub-skill when a task is about EasyR1 input data, multimodal prompt formatting, or rule-based reward functions. It is a routing entry point; load the linked references for the concrete contracts and validation steps.

Use this for

• Preparing text, image-text, multi-image, text-image mixed, or video dataset rows for EasyR1.
• Choosing and adapting Jinja prompt templates for math, R1-V, DAPO, and Android GUI-style tasks.
• Writing reward functions that EasyR1's AutoRewardManager can load through worker.reward.reward_function.
• Checking batch versus sequential reward functions and the required overall score key.
• Planning Android GUI number-game data/reward work without assuming a runnable device, game service, or VLM endpoint.

Route elsewhere

• Training launch overrides, Ray/FSDP/vLLM sizing, KL/loss algorithm selection, and full job commands belong to the sibling training-workflows sub-skill.
• Low-level DataProto construction, tensor/non-tensor batch debugging, and padding APIs belong to the sibling core-apis sub-skill.
• Checkpoint merging and Hugging Face export belong to the sibling checkpoint-export sub-skill.

Files to load

• Data and reward formats: concrete dataset columns, prompt template patterns, reward signatures, score keys, and validation commands.
• Android GUI cookbook: reference-only prerequisites, data shape, prompt, and reward design for the number-game workflow.
• Troubleshooting: common failures for datasets, templates, reward imports, score dictionaries, mixed media rows, remote datasets, and backend limits.
• Reward smoke script: deterministic local checks for built-in reward contracts, mixed row guards, and optional custom module.py:function validation.

Fast path

1. Identify the modality: text-only, image-text, multi-image, text-image mixed, or video.
2. Normalize rows to include the configured prompt and answer columns; use media lists only when the prompt contains the matching placeholder.
3. Select or write a Jinja prompt template that renders a single content variable and leaves chat-template wrapping to the tokenizer or processor.
4. Write a reward function with explicit REWARD_TYPE = "batch" or REWARD_TYPE = "sequential"; every returned score dictionary must contain numeric overall.
5. Run the bundled smoke script before launching training.

Full EasyR1 training is a CUDA workflow that also needs the full flash-attn/vLLM/Ray runtime. CPU/API smoke checks validate data and reward contracts only; they do not prove that a full training job will run.