MATH

The 'MATH' dataset is a benchmark that contains mathematical problems used to evaluate the performance of large language models in solving complex reasoning tasks.

Papers using MATH (10)

DynaGraph: Lightweight Multi-Model Interaction Framework via Dynamic Topological Reconfiguration2026KV-PRM: Efficient Process Reward Modeling via KV-Cache Transfer for Multi-Agent Test-Time Scaling2026Graph-of-Agents: A Graph-based Framework for Multi-Agent LLM Collaboration2026CARD: Towards Conditional Design of Multi-agent Topological Structures2026Plan Then Action:High-Level Planning Guidance Reinforcement Learning for LLM Reasoning2025How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs2025Putting the Value Back in RL: Better Test-Time Scaling by Unifying LLM Reasoners With Verifiers2025DebFlow: Automating Agent Creation via Agent Debate2025GATE: Graph-based Adaptive Tool Evolution Across Diverse Tasks2025Q*: Improving Multi-step Reasoning for LLMs with Deliberative Planning2024 · 9 cites