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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 “MATH500”

Research · Language models
Entropy signals guide efficient reasoning repair in LLMs
REWIND: Uncertainty-Guided Trajectory Repair for Test-Time Reasoning
Ding et al. introduce REWIND, a training-free method that uses token entropy dynamics to select optimal points for regenerating suffixes, improving reasoning accuracy across multiple models and benchmarks.
MATH500: 77.6% accuracy (as reported)
15 Oct 2026The Hong Kong University of Science and Technology (Guangzhou), University of Edinburgh, Huawei