AMC-23
Emerging8papers using it
2025first seen
The 'AMC-23' dataset is used to evaluate the performance of models in long-context reasoning tasks, specifically in the context of vision-text compression methods.
Papers using AMC-23 (8)
- VTC-R1: Vision-Text Compression for Efficient Long-Context ReasoningOff-the-Shelf LLMs as Process Scorers: Training-Free Alternative to PRMs for Mathematical ReasoningLong Chain-of-Thought Compression via Fine-Grained Group Policy OptimizationPrompting Test-Time Scaling Is A Strong LLM Reasoning Data AugmentationSocratic-Zero : Bootstrapping Reasoning via Data-Free Agent Co-evolutionReinforcement Learning for Reasoning in Small LLMs: What Works and What
Doesn'tConfidence Is All You Need: Few-Shot RL Fine-Tuning of Language ModelsSkill-Targeted Adaptive Training