Synthetic data augmentation helps language models learn new knowledge in data-constrained domains. However, naively scaling existing synthetic data methods by training on more synthetic tokens or using stronger generators yields diminishing returns below the performance of RAG. To break the RAG ceiling, we introduce Synthetic Mixed Training, which combines synthetic QAs and synthetic documents. This leverages their complementary training signals, and enables log-linear improvements as both synthetic data volume and generator strength increase. This allows the model to outperform RAG by a 2.6% relative gain on QuaLITY, a long-document reading comprehension benchmark. In addition, we introduce Focal Rewriting, a simple technique for synthetic document generation that explicitly conditions document generation on specific questions, improving the diversity of synthetic documents and yielding a steeper log-linear scaling curve. On QuaLITY, our final recipe trains a Llama 8B model that outperforms RAG by 4.4% relatively. Across models and benchmarks (QuaLITY, LongHealth, FinanceBench), our training enables models to beat RAG in five of six settings, outperforms by 2.6%, and achieves a 9.1% gain when combined with RAG.

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/

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.