MASPRM: A Process Reward Model for Multi-Agent Systems

Jul 16, 2026

Researchers introduce MASPRM, a process reward model designed to score intermediate messages in multi-agent systems, enabling more effective inference-time search. Unlike prior approaches, MASPRM is trained without human step-level annotations and instead uses terminal outcome rewards from multi-agent MCTS rollouts. The model demonstrates improvements of up to 14.5 points over outcome reward models on benchmarks such as GSM8K, MATH, MMLU, and LogiQA.

Why it matters: MASPRM enables step-level credit assignment in multi-agent systems, addressing inefficiencies in inference-time search and improving performance on complex reasoning tasks.

Full story at: arXiv Multiagent Systems ↗