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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 →

1 result for “MATH-500”

Research · Reinforcement learning
Preserving multiple reasoning strategies prevents late-stage collapse in RLVR
All Work And No Play Makes Jack a Dull Boy: Understanding and Preventing Catastrophic Strategy Collapse in RLVR
The authors connect abrupt performance loss to strategy concentration, derive an early-warning metric and test a strategy-balancing training method.
MATH-500: 86.8% Mean@4 Accuracy (as reported)
7 Oct 2026Peking University