Leveraging multi-agentic frameworks to enhance large language models (LLMs) has demonstrated significant potential recently, with most existing studies focusing on prompting and developing workflows with frozen LLMs. In this paper, we aim to further unleash the power of such multi-agentic frameworks for post-training LLMs for better collaboration. Specifically, we develop a new paradigm of Multi-Agent Post-co-training for collaborative LLMs with Reinforcement Learning (MAPoRL). In MAPoRL, multiple LLMs first generate their own responses and engage in discussions to collaboratively enhance the final response output; the final output is then scored by a verifier, where the scores serve as the reward and is maximized through multi-agent RL. Additionally, MAPoRL also reshapes the reward above with additional incentives to encourage corrective and persuasive outputs in the discussions. A key novelty from most existing LLM post-training paradigms is the advocacy of co-training multiple LLMs together, and the use of RL for better generalization. Accompanied by a few analytical insights, our experiments show that training single LLMs solely is insufficient for encouraging collaboration, while multi-agent co-training can significantly enhance the collaboration performance across multiple datasets, with generalization to unseen domains, compared to that of multiple LLMs before post-training.

Large Language Models (LLMs) as agents require careful behavioral adaptation. While adept at reactive tasks (e.g., medical reasoning), LLMs often struggle with proactive engagement, like unprompted identification of critical missing information or risks. We introduce **BehaviorBench**, a comprehensive dataset to evaluate agent behaviors across a clinical assistance spectrum. To rigorously test the current models, we also introduce **BehaviorBench-Hard**, a challenging subset where the performance of state-of-the-art models drops significantly, revealing weaknesses. To address these challenges, we propose **BehaviorSFT**, a novel training strategy using behavioral tokens to explicitly condition LLMs for dynamic behavioral selection which boosts performance on both benchmarks. Crucially, a blind clinician evaluation confirmed that our trained agents exhibit more realistic clinical behavior, striking a superior balance between helpful proactivity and necessary restraint versus standard fine-tuning or explicitly instructed agents. Project Page: https://behavior-adaptation.github.io/