Math Reward Eval

math-reward-eval

SPARK: Synergistic Policy And Reward Co-Evolving Framework — Liu et al. (2025) (arXiv:2509.22624, 2025)

What this evaluates

Evaluates multimodal and language-only models on mathematical reasoning, self-judgment/reward accuracy, and general multimodal capabilities. It measures how well a model can solve complex problems, verify its own answers, and generalize across diverse domains without external reward models.

Datasets

• MathVista — total ?; splits: test (-1)
• GSM8k — total ?; splits: test (-1)
• RewardBench2 — total ?; splits: test (-1)
• VL-RewardBench — total ?; splits: test (-1)
• MMBench — total ?; splits: test (-1)
• MMStar — total ?; splits: test (-1)

Metrics

• accuracy (primary) — range: percent
  • Percentage of instances where the model's predicted answer or correctness judgment matches the ground truth. Computed as (correct predictions / total instances) * 100.

Input / output format

Input: Text and/or image prompts containing mathematical problems or general multimodal questions.

Output: Final answer string or a binary correctness judgment (correct/incorrect).

Scoring recipe

def compute_accuracy(predictions, gold):
    correct = 0
    for p, g in zip(predictions, gold):
        if normalize_answer(p) == normalize_answer(g):
            correct += 1
    return (correct / len(gold)) * 100

Common pitfalls

• Reward and math benchmarks often require strict formatting for answer extraction; minor formatting differences (e.g., fractions vs decimals, extra whitespace) can cause false negatives if not normalized.
• Self-judgment accuracy evaluation requires the model to output both reasoning steps and a binary correctness label, which can be affected by chain-of-thought verbosity or inconsistent labeling formats.

Evidence (verbatim from paper)

We evaluate the model’s judgment ability by measuring its accuracy in determining whether its own answers are correct. Based on these judgments, we compute recall, precision, and F1 scores.

Citation

@misc{liu2025spark,
  title={SPARK: Synergistic Policy And Reward Co-Evolving Framework},
  author={Liu et al. (2025)},
  year={2025},
  note={arXiv:2509.22624}
}

• arXiv: 2509.22624