Reinforcement learning (RL) has shown promise for training agentic models that engage in dynamic, multi-turn interactions. However, the value of such agents ultimately depends on their ability to assist users with diverse goals, preferences, and evolving interaction patterns. We introduce **UserRL**, a reproducible and extensible testbed for training and evaluating user-centric agents through standardized gym environments paired with simulated users. UserRL supports flexible reward specification by decoupling turn-level reward shaping from trajectory-level scoring, enabling controlled studies of credit assignment in multi-turn RL. Across Qwen3 models, our experiments show that SFT cold start provides an effective initialization for user-centric RL; trajectory-level reward-to-go scoring improves both effectiveness and interaction efficiency; and stronger simulated users improve training, while open-source simulators remain a cost-effective alternative with positive cross-simulator and preliminary real-user transfer. These results highlight reward design and user simulation as key to user-centric agent training, positioning UserRL as a practical foundation for interactive agent RL.

Large Language Model agents increasingly solve tool-use tasks, yet often fail to determine what users actually want when goals are underspecified, incremental, or indirectly expressed. We introduce **UserBench**, a controlled Gymnasium environment for evaluating proactive preference elicitation in multi-turn, tool-grounded interactions. Instantiated in travel planning, UserBench hides user preferences behind implicit natural-language cues and requires agents to ask targeted clarification questions, search structured option databases, and select user-aligned options. Evaluating leading open- and closed-source models reveals a large gap between tool execution and user understanding: models achieve high valid-search rates but elicit only a minority of hidden preferences, and even stronger models remain far below a human oracle. Further analysis shows that many additional interaction turns are broad or non-informative rather than preference-relevant. UserBench provides a reproducible testbed for studying and training user-centric agent interaction while intentionally abstracting away full travel negotiation and infeasible-constraint recovery.

Large Action models are essential for enabling autonomous agents to perform complex tasks. However, training such models remains challenging due to the diversity of agent environments and the complexity of noisy agentic data. Existing infrastructure offers limited support for scalable, agent-specific fine-tuning and standardized agent data processing. We introduce ActionStudio, a lightweight and extensible data and training framework designed for large action models. ActionStudio unifies diverse agent trajectories using our proposed Unified Format 2.0, supports a range of training workflows with optimized multi-node distributed setup, and integrates robust preprocessing and real-time verification tools. ActionStudio demonstrates up to 9× higher throughput compared to existing agentic training frameworks, and our trained models yield top performances across public and realistic agent benchmarks. To support the broader research community, we open-source the ActionStudio framework and release actionstudio-98k, a curated dataset of 98k high-quality trajectories.

The rapid adoption of Large Language Models (LLMs) as intelligent agents has underscored the necessity for robust evaluation frameworks capable of assessing agent performance in realistic, interactive environments. Existing evaluation methodologies often suffer from limitations such as static task benchmarks, limited scope, and inadequate integration with practical applications. In response, we introduce MCPEval, an open-source, Model Context Protocol (MCP)-based evaluation framework specifically tailored for comprehensive and systematic assessment of LLM-powered agents. MCPEval standardizes evaluations across diverse domains through automated task generation and verification, supports multiple performance metrics, and integrates seamlessly with native agent capabilities. We empirically validate the effectiveness of MCPEval across five distinct real-world domains, highlighting significant variations in performance across various LLM architectures and prompting strategies. Our results illustrate the framework’s capacity to uncover nuanced performance patterns and identify domain-specific strengths and weaknesses, providing valuable insights beyond traditional binary success metrics. We publicly release MCPEval to foster reproducible research and promote standardized evaluation practices within the LLM agent community.

Transformer-based architectures are the model of choice for natural language understanding, but they come at a significant cost, as they have quadratic complexity in the input length, require a lot of training data, and can be difficult to tune. In the pursuit of lower costs, we investigate simple MLP-based architectures. We find that existing architectures such as MLPMixer, which achieves token mixing through a static MLP applied to each feature independently, are too detached from the inductive biases required for natural language understanding. In this paper, we propose a simple variant, HyperMixer, which forms the token mixing MLP dynamically using hypernetworks. Empirically, we demonstrate that our model performs better than alternative MLP-based models, and on par with Transformers. In contrast to Transformers, HyperMixer achieves these results at substantially lower costs in terms of processing time, training data, and hyperparameter tuning.