---

name: "data-and-rewards" description: "Validate VLM-R1 JSONL datasets, map image roots, and choose or test reward methods." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

data-and-rewards

Use this sub-skill when you need to:

• validate single-image, multi-image, or text-only JSONL rows
• map relative image paths against paired image roots
• choose between file-level reward_method and row-level accu_reward_method
• explain answer formats for label, math, bbox, and OD fixtures
• run lightweight bbox scoring on tiny synthetic examples

Start here

1. Read references/data-formats.md.
2. Read references/reward-functions.md.
3. Read references/troubleshooting.md.
4. Run scripts/validate_jsonl_dataset.py for schema checks.
5. Run scripts/score_bbox_outputs.py for tiny bbox fixtures.

Route elsewhere

• Training command construction, launch flags, or DeepSpeed details -> training-workflows
• Model module interfaces or processor wiring -> model-modules
• Saved-output evaluation workflows -> evaluation

What this sub-skill covers

• one JSON object per line
• image as a string or list of strings
• conversations with a question turn and an answer turn
• colon-separated file, image-root, and method pairing
• reward registry names: accuracy, format, length, repetition
• accuracy methods: default, mcq, yes_no, llm, map, math, weighted_sum, od_ap, od_ap50, odLength, all_match

Guardrails

• Keep image paths relative to the paired image root.
• Keep answers concise; the loader wraps the stored answer in <answer>...</answer> internally.
• Do not tell future agents to inspect the source repository for these rules.