umd-zhou-lab/AVQA-Audio-Rubrics

AVQA Audio-Reasoning Rubrics

Project Page | Paper | Code

Audio-grounded, binary-evaluable evaluation rubrics for the full AVQA training set, generated for process-level reward modeling in audio reasoning RL (e.g. GRPO / RLHF with rubric-as-reward).

Each training question is annotated with 5 rubrics, one per evaluation facet, that judge the quality of an audio-reasoning response — not just final answer correctness. The rubrics are designed to be scored Yes/No by an LLM judge that has access to the same audio.

Why

Outcome-only rewards (answer correct / wrong) cause reasoning collapse in RL: the policy learns to shortcut and stops producing meaningful reasoning. These rubrics provide a dense, process-level signal that rewards grounded, focused, domain-appropriate reasoning, preventing collapse and improving generalization.

Format

One JSON object per line:

{
  "id": "avqa-train-000123",
  "audio_path": "audio/train/<clip>.wav",
  "question": "What happened in the audio?
Choices:
A. ...
B. ...",
  "reference_answer": "B. Yacht consignment",
  "rubric": {
    "question_domain": "audio_reasoning/environmental_sound",
    "rubrics": [
      {"category": "Auditory Evidence Identification & Grounding", "criterion": "...", "weight": 0.25},
      {"category": "Cross-Cue Verification & Distractor Elimination", "criterion": "...", "weight": 0.20},
      {"category": "Reasoning Clarity & Flow", "criterion": "...", "weight": 0.20},
      {"category": "Reasoning Focus & Efficiency", "criterion": "...", "weight": 0.15},
      {"category": "Domain-Specific Audio Techniques", "criterion": "...", "weight": 0.20}
    ],
    "reference_score": 0.4
  }
}

The 5 rubric categories (each appears exactly once per question)

1. Auditory Evidence Identification & Grounding — identifying concrete audio cues actually present and anchoring claims to them.
2. Cross-Cue Verification & Distractor Elimination — triangulating multiple cues; ruling out named distractor options by absent characteristic sounds.
3. Reasoning Clarity & Flow — coherent observation → inference → conclusion structure.
4. Reasoning Focus & Efficiency — alignment with the question objective; no tangential over-enumeration.
5. Domain-Specific Audio Techniques — context-optimal vocabulary/methods (music-theoretic, phonetic/prosodic, temporal counting).

Properties: the 5 weights sum to 1.0; reference_score (weights the reference answer would satisfy) is kept strictly below 0.5 so a stronger response has room to outscore the reference. No rubric evaluates final-answer correctness — that is handled by a separate ground-truth reward.

Audio

This dataset contains annotations only. The audio_path field references clips from the original AVQA dataset. Download the original audio from Joysw909/AVQA and join by audio_path / id:

hf download Joysw909/AVQA --repo-type dataset --local-dir ./AVQA

Generation

• Generator: Gemini 3.1 Pro (reasoning_effort=medium, temperature=0)
• Each rubric is conditioned on the actual audio clip + question + reference answer.
• Questions are phrased for the audio-only setting (the original "video" phrasing is normalized to "audio").

Citation

If you use these rubrics, please cite this dataset and the original AVQA dataset.

@article{yu2026audiorubrics,
  title={Reinforcement Learning with Evolving Rubrics for Audio Reasoning},
  author={Yu, Fangxu and Feng, Tao and Min, Dehai and Lin, Zinan and Liu, Xiaodong and Xu, Weijia and Yu, Philip S. and Liu, Ge and Zhou, Tianyi and Gao, Jianfeng},
  journal={arXiv preprint},
  year={2026}
}

License

CC BY 4.0.