Xingyao Wang

Despite rapid progress, current AI agents are primarily optimized for isolated task completion. We argue for a paradigm shift toward training agents as collaborators that communicate and adapt to people. To facilitate this shift in real-world complex applications, we first formalize three dimensions of collaborative AI agents: Productivity, Proactivity, and Personalization (PPP). We then introduce UserVille, an interactive environment with configurable LLM-based user simulators and user-centric feedback to evaluate these dimensions. Building on this setup, we present a multi-objective reinforcement learning framework that optimizes all three dimensions using rewards from task outcomes, question effort, and preference adherence. On two consequential real-world agentic tasks (namely, SWE Bench and BrowseComp Plus), PPP trained agents outperform strong LLM baselines (including GPT-5) with an average gain of 16.7 points, ask targeted questions, and generalize to unseen preferences and tasks. A follow-up user study further validates the importance of incorporating user centric feedback in training AI agents, and building such collaborative agents is not only important for making agents more effective, but also easier to supervise.

Academic benchmarks for coding agents tend to reward autonomous task completion, measured by verifiable rewards such as unit-test success. In contrast, real-world coding agents operate with humans in the loop, where success signals are typically noisy, delayed, and sparse. How can we bridge this gap? In this paper, we propose a process to learn a ``critic'' model from sparse and noisy interaction data, which can then be used for inference-time scaling and training-time data curation. Specifically, we introduce Critic Rubrics, a rubric-based supervision framework with 24 behavioral features that can be derived from human-agent interaction traces alone. Using a semi-supervised objective, we can then jointly predict these rubrics and sparse human feedback (when present). In experiments, we demonstrate that, despite being trained primarily from trace-observable rubrics and sparse real-world outcome proxies, these critics improve best-of-N reranking on SWE-bench (Best@8 +15.9 over Random@8 over the rerankable subset of trajectories), enable early stopping (+17.7 with 83\% fewer attempts), and support training-time data curation of trajectories.