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MAPoRL: Multi-Agent Post-Co-Training for Collaborative Large Language Models with Reinforcement Learning
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Claims the paper makes
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Claims other papers make about this one
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In the citing paper
“Methods such as MAGRPO, MAPoRL, MLPO, ReMA, FlowReasoner, Chain-of-Agents, and SPIRAL (Liu et al., 2025l;Park et al., 2025a;Estornell et al., 2025a;Wan et al., 2025b;Gao et al., 2025b;Li et al., 2025h;Liu et al., 2025b) jointly optimize collaboration and reasoning behaviors, enabling emergent division of labor and communication conventions.”
From The landscape of agentic reinforcement learning for LLMs: a survey (2025) · cites this paper as Park et al., 2025a
In the citing paper
“MAPoRL (Park et al., 2025a) extends MAD by verifying debate responses and using validation outcomes as RL rewards to improve collaborative reasoning.”
From The landscape of agentic reinforcement learning for LLMs: a survey (2025) · cites this paper as Park et al., 2025a
In the citing paper
“MAPoRL (Park et al., 2025a) | ✔ | ✔ | PPO | GitHub |”
From The landscape of agentic reinforcement learning for LLMs: a survey (2025) · cites this paper as Park et al., 2025a
In the citing paper
“MAPoRL (Park et al., 2025a) | ✔ | ✔ | PPO | GitHub |”
From The landscape of agentic reinforcement learning for LLMs: a survey (2025) · cites this paper as Park et al., 2025a
In the citing paper
“MAPoRL (Park et al., 2025a) | ✔ | ✔ | PPO | GitHub |”
From The landscape of agentic reinforcement learning for LLMs: a survey (2025) · cites this paper as Park et al., 2025a
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
The landscape of agentic reinforcement learning for LLMs: a survey
Zhang, G., Geng, H., Yu, X., Yin, Z., Zhang, Z., Tan, Z., Zhou, H., Li, Z., Xue, X., Li, Y., et al. (2025) · arXiv · doi:10.48550/arxiv.2509.02547 · Cited by 229
Fang, J., Peng, Y., Zhang, X., Wang, Y., Yi, X., Zhang, G., Xu, Y., Wu, B., Liu, S., Li, Z., et al. (2025) · arXiv · doi:10.48550/arxiv.2508.07407 · Cited by 6
Multi-Agent Debate Strategies: Survey, Taxonomy, and Challenges
Motger, Q., Oriol, M., Marco, J., & Franch, X. (2026) · arXiv · doi:10.48550/arxiv.2607.26212 · Cited by 5
Metadata
- Authors
- Park, C., Han, S., Guo, X., Ozdaglar, A. E., Zhang, K., & Kim, J.-K.
- Venue
- Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2025
- Pages
- 30215-30248
- Cited by
- 10
- References
- 0
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
