The trio is shining 500 light-years away from Earth. Breakthroughs, discoveries, and DIY tips sent six days a week. NASA's Hubble Space Telescope has captured a trio of young stars in the process of becoming their best selves in the constellation Scorpius. Posted to the agency's site on January 16 as part of its Hubble Stellar Construction Zones series, the three T Tauri stars--seen at the bottom right, upper center, and left along with many other stellar objects in the background--are forming inside the hazy Lupus 3 cloud about 500 light-years from Earth. While the image appears somewhat serene, the interior forces at play are anything but tranquil.

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Leveraging multiple large language models (LLMs) to build collaborative multi-agentic workflows has demonstrated significant potential. However, most previous studies focus on prompting the out-of-the-box LLMs, relying on their innate capability for collaboration, which may not improve LLMs' performance as shown recently. In this paper, we introduce a new post-training paradigm MAPoRL (Multi-Agent Post-co-training for collaborative LLMs with Reinforcement Learning), to explicitly elicit the collaborative behaviors and further unleash the power of multi-agentic LLM frameworks. In MAPoRL, multiple LLMs first generate their own responses independently and engage in a multi-turn discussion to collaboratively improve the final answer. In the end, a MAPoRL verifier evaluates both the answer and the discussion, by assigning a score that verifies the correctness of the answer, while adding incentives to encourage corrective and persuasive discussions. The score serves as the co-training reward, and is then maximized through multi-agent RL. Unlike existing LLM post-training paradigms, MAPoRL advocates the co-training of multiple LLMs together using RL for better generalization. Accompanied by analytical insights, our experiments demonstrate that training individual LLMs alone is insufficient to induce effective collaboration. In contrast, multi-agent co-training can boost the collaboration performance across benchmarks, with generalization to unseen domains.