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A head-to-head benchmark comparison of Eurus-2-7B-PRIME across 2 evaluations.
Dense process rewards have proven a more effective alternative to the sparse outcome-level rewards in the inference-time scaling of large language models (LLMs).
Full Eurus-2-7B-PRIME specs →| Benchmark | Eurus-2-7B-PRIME |
|---|---|
| math | |
| AIME 2024 | 26.7% |
| MATH-500 | 79.2% |
Best result per row highlighted in cyan. Each benchmark links to its definition and sources; each model links to its full scorecard.
This comparison covers 2 benchmarks on which at least one of the selected models has a published score.
Scores are aggregated from official model cards, technical reports and standard public evaluations, and link back to each benchmark's source. They are updated as new results are published.
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