Large language models are increasingly deployed in applications that act on users' long-term behavior histories, from personal assistants to recommendation and advertising systems.
These applications demand the user dimension of LLM intelligence: inferring a person's preferences, intent, and likely future actions from weeks to years of behavior data.
Yet current models are trained on static text or synthetic data, neither of which captures how user behavior evolves over time.
Real-world behavior records, already available at scale on content platforms and in personal-assistant agents, offer a natural supervisory signal.
We propose user behavior as supervision and introduce Vela, a framework that converts real user behavior data into trainable tasks and trajectories.
From the anonymized behavior records of 100,000 users, Vela generates 100,000 tasks across 91 real-world task templates;
difficulty-based filtering and trajectory acceptance criteria retain approximately 75,000 tasks with selected training trajectories.
Fine-tuning Qwen3.6-35B-A3B on these trajectories improves performance over the base model by up to 11.5 points on user-related tasks and by an average of 4 points on general benchmarks.
Training on more user behavior data further improves user-related performance, and reinforcement learning after fine-tuning outperforms reinforcement learning from the base model by up to 7.7 points.
These gains suggest that real-world user behavior is a scalable source of supervision for both user-centered capabilities and the broader intelligence of LLMs.