MATH-500

Headlines · 2

1. 6d
   ThinkBit proposes two-bit quantization for reasoning models

   Rhea Sanjay Sukthanker and Pashmina Cameron submitted the ThinkBit paper to arXiv on October 26, 2026. The framework uses layer-specific bit allocation, Gumbel-softmax-based scalar quantization, scale recalibration and quantization-aware training to preserve reasoning while compressing weights to two bits. The authors report that a 0.52 GB Qwen3-1.7B checkpoint scored 46.7 on MATH-500 after recalibration and 73.0 after QAT, while a 3.74 GB model scaled to 14B scored 94.8 versus 97.5 for the 29.5 GB bfloat16 model; these results remain author claims from a single paper.

   Hugging Face · Papers · 🔥 0
2. Oct 21
   Controlled study examines learned unmasking for diffusion language models

   A preprint submitted to arXiv on October 22, 2026 presents a controlled study of learned unmasking policies for masked diffusion language models, separating the effects of teacher labels, imitation states, policy inputs, capacity, and compute overhead. Its 856K-parameter trajectory-contraction policy achieved the reported best accuracy–NFE frontier and highest GSM8K throughput on LLaDA-8B-Instruct, with competitive results on MATH-500 and HumanEval and transfer largely to Dream-7B-Instruct.

   Hugging Face · Papers · 🔥 0

Experience and discussion from the community

Share my MATH-500 experienceAsk about MATH-500