Reinforcement Learning (RL) is essential for evolving Large Language Models (LLMs) into autonomous agents capable of long-horizon planning, yet a practical recipe for scaling RL in complex, multi-turn environments remains elusive. This paper presents a systematic empirical study using TravelPlanner, a challenging testbed requiring tool orchestration to satisfy multifaceted constraints. We decompose the agentic RL design space along 5 axes: reward shaping, model scaling, data composition, algorithm selection, and environmental stability. Our controlled experiments yield 7 key takeaways, e.g., (1) reward and algorithm choices are scale-dependent as smaller models benefit from staged rewards and enhanced exploration, whereas larger models converge efficiently with simpler dense rewards, (2) ~1K training samples with a balanced difficulty mixture mark an empirical sweet spot for balancing in-domain and out-of-domain performance under our primary setting, and (3) environmental stability is critical to prevent policy degradation. Based on our distilled recipe, our RL-trained models achieve state-of-the-art performance on TravelPlanner, outperforming leading LLMs.

Large Multimodal Models (LMMs), characterized by their humanoid perception and generation capabilities, have been widely applied in critical physical world scenarios, yet their uncertainty remains mostly unknown. In this paper, we aim to answer three substantial questions: (1) Can we explicitly capture the uncertainty of LMMs? (2) How can we tackle the reasoning complexity arising from prompts across various modalities? (3) How can we quantify the uncertainty of LMMs when their responses extend to multiple modalities, beyond mere text? To answer these questions, we propose Uncertainty-o: (1) Uncertainty-o can explicitly estimate uncertainty of LMMs, with prompts and responses across five modalities, namely text, image, audio, video, and point cloud. (2) We propose multimodal prompt perturbation, where empirical studies indicate that semantically equivalent perturbations are more effective for eliciting LMM uncertainty. (3) We introduce multimodal semantic uncertainty, enabling uncertainty estimation when LMMs generate multimodal responses. Extensive quantitative and qualitative analyses validate the efficacy of Uncertainty-o in reliably estimating the uncertainty inherent in LMMs.

Document Understanding is a foundational AI capability with broad applications, and Document Question Answering (DocQA) is a key evaluation task. Traditional methods convert the document into text for processing by Large Language Models (LLMs), but this process strips away critical multi-modal information like figures. While Large Vision-Language Models (LVLMs) address this limitation, their constrained input size makes multi-page document comprehension infeasible. Retrieval-augmented generation (RAG) methods mitigate this by selecting relevant pages, but they rely solely on semantic relevance, ignoring logical connections between pages and the query, which is essential for reasoning and accurate answers.To this end, we propose MoLoRAG, a logic-aware retrieval framework for multi-modal, multi-page document understanding. By constructing a page graph that captures contextual relationships between pages, a lightweight VLM performs graph traversal to retrieve relevant pages, including those with logical connections often overlooked. This approach combines semantic and logical relevance to deliver more accurate retrieval. After retrieval, the top-K pages are fed into arbitrary LVLMs for question answering. To enhance flexibility, MoLoRAG offers two variants: a training-free solution for easy deployment and a fine-tuned version to improve logical relevance checking. Experiments on four DocQA datasets demonstrate average improvements of 9.68% in accuracy over LVLM direct inference and 7.44% in retrieval precision over baselines. Codes and datasets are released at https://github.com/WxxShirley/MoLoRAG.