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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 →

4 results for “MATH-500”

Research · Efficiency & systems
Scalar two-bit weight quantization can preserve language model reasoning accuracy
ThinkBit: Search Codewords, Recalibrate Scales, Select by Reasoning
ThinkBit, a five-stage framework, keeps MATH-500 accuracy within a few points of bfloat16 on models up to 14B while shrinking memory traffic by up to 8x.
MATH-500: 94.8% accuracy (as reported)
27 Oct 2026Microsoft Applied Sciences Group
Research · Language models
Training on self-generated answers improves reasoning in low-bit language models
Right On Q: Recovering Reasoning under Quantization via Self-Generated Trajectories
Across three Qwen3 model sizes at 2- and 3-bit precision, OnQ improves benchmark accuracy while reducing truncated and repetitive responses.
MATH-500: 69.33% accuracy (as reported)
22 Oct 2026University of Michigan, Ann Arbor, University of California, Santa Barbara, Meta AI
Research · Efficiency & systems
Low-rank KV cache compression preserves all tokens during long reasoning
iS-KV: Online Low-Rank KV Cache Compression via Block-Incremental SVD
iS-KV uses block-incremental SVD to update both basis and coefficients, avoiding representation drift and matching original model accuracy at 4-5x compression on DeepSeek-R1 and Qwen3-8B.
MATH-500: 89.2% accuracy (as reported)
5 Oct 2026The Hong Kong University of Science and Technology (Guangzhou), Guangdong OPPO Mobile Telecommunications Corp., Ltd., Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences
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

MATH-500

Scalar two-bit weight quantization can preserve language model reasoning accuracy

Training on self-generated answers improves reasoning in low-bit language models

Low-rank KV cache compression preserves all tokens during long reasoning

Language models can adapt compressed memory without growing their dictionaries