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Benchmark

MATH-500

Reported in 46 papers on Zotpaper. The latest, each as the paper states it:

Reasoning models can be trained to stop when they know the answer91.8% Accuracy
Language models learn harder problems but can forget easier ones93.24% Pass@128
Parallel training method speeds language model inference by 3.5x4.57 acceptance length

MATH500 (20)All benchmarks →

2 results for “MATH-500”

Research · Efficiency & systems
Language models can adapt compressed memory without growing their dictionaries
FAD-KV: Adaptive Dictionary KV Caching for Efficient Reasoning Decoding
The authors combine faster selection of compression components with reuse of inactive components, keeping the dictionary fixed in size as generation continues.
MATH-500: 85.7% full-OMP support recall (as reported)
30 Oct 2026Rensselaer Polytechnic Institute, IBM Research
Research · Language models
Language models gain from varied answers, not necessarily altered weights
Rethinking Neural Thickets Without Weight Perturbations
Dufour and colleagues show that changing output scores, selecting useful variants and combining their answers can match or exceed ensembles built by perturbing model weights.
MATH-500: 63.3% accuracy (as reported)
30 Oct 2026Kyutai, Meta FAIR, LIGM