Add a Reward Scorer

One new file under verl_omni/utils/reward_score/, then select it from the run config — there is no code registration step.

The signature, return, failure, and caching contracts live in the reward rule; read it first, this skill does not repeat it. For the machinery around the scorer see docs/algo/async_reward.md (reward-loop workers, resource pools, config reference) and docs/start/http_scorer.md (the request/response protocol for a remote scorer service).

Step 1 — Copy the closest existing scorer

grep -rn "^async def compute_score\|^def compute_score" verl_omni/utils/reward_score/*.py

genrm_ocr.py::compute_score_ocr is the most-copied remote pattern and the one most examples/ scripts use; jpeg_compressibility.py is the minimal rule-based one; hpsv3_reward.py is the reference for a locally-loaded preference model. reward_utils.py holds the tensor/PIL conversion helpers — use them rather than re-deriving the conversion.

Step 2 — Write it

Apache 2026 header, a module docstring crediting upstream if the score is adapted, and one compute_score / compute_score_<name> entrypoint matching the rule's keyword contract. Nothing else is required of the file.

Step 3 — Select it from a run

The config keys and their semantics (custom_reward_function vs reward_functions.<key> with weight / required, and how extra keys become kwargs) are in the reward rule.

Copy a working invocation from examples/ rather than assembling one by hand — examples/flowgrpo_trainer/sd35/run_sd35_medium_drm_lora.sh is the multi-reward reference.

Step 4 — Test

tests/utils/reward_score/test_<name>_on_cpu.py, asserting the score is finite and in range for a synthetic tensor; mock the transport for an async scorer (run-cpu-tests).

def test_score_is_finite_on_cpu():
    image = torch.randint(256, (3, 64, 64), dtype=torch.uint8)
    out = <name>.compute_score(solution_image=image)
    assert 0.0 <= out["score"]