Scaling Multiagent Systems with Process Rewards

• URL: http://arxiv.org/abs/2601.23228v2
• Date: Wed, 04 Feb 2026 00:14:54 GMT
• Title: Scaling Multiagent Systems with Process Rewards
• Authors: Ed Li, Junyu Ren, Cat Yan,
• Abstract summary: We propose finetuning multiagent systems with per-action process rewards from AI feedback (MAPPA)<n>We demonstrate our approach on competition math problems and tool-augmented data analysis tasks.<n>For data analysis tasks, our method improves success rate by +16.7pp while quality metrics improve by up to 47%.
• Score: 0.46729918593868963
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While multiagent systems have shown promise for tackling complex tasks via specialization, finetuning multiple agents simultaneously faces two key challenges: (1) credit assignment across agents, and (2) sample efficiency of expensive multiagent rollouts. In this work, we propose finetuning multiagent systems with per-action process rewards from AI feedback (MAPPA) to address both. Through assigning credit to individual agent actions rather than only at task completion, MAPPA enables fine-grained supervision without ground truth labels while extracting maximal training signal from each rollout. We demonstrate our approach on competition math problems and tool-augmented data analysis tasks. On unseen math problems, MAPPA achieves +5.0--17.5pp on AIME and +7.8--17.2pp on AMC. For data analysis tasks, our method improves success rate by +16.7pp while quality metrics improve by up to 47%, validating that per-action supervision can lead to improvements across different multiagent systems on various domains. By addressing these challenges, our work takes a first step toward scaling multiagent systems for complex, long-horizon tasks with minimal human supervision.

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