Models, methods, and the machinery of intelligence.
Hugging Face
Verdict: NotableRecent advances in large language models have made automatic game generation increasingly feasible, yet reliably improving generated games beyond a playable version remains challenging.
Evolutionary search with large language models (LLMs) can stall when progress requires external knowledge the model lacks.
Reinforcement learning can turn one language model into several specialists, each excellent at a single skill such as mathematics, coding or following instructions, but users need one model with all of these skills.
As interactive 3D worlds are increasingly used to study intelligent behavior, it becomes important to develop efficient pipelines for identifying anomalies in these simulated environments, such as floating objects…
On-policy distillation (OPD) trains a student to match the teacher's next-token distributions on the student's own trajectories and has yielded substantial empirical gains.
Direct-decision models turn text into low-latency structured labels and scores, making them attractive for classification and automatic evaluation.
Answering questions about long videos often requires connecting events involving the same objects across hours or days.
Self-evolving search agents build their own training curricula by jointly optimizing a proposer that generates questions and a solver that answers them.
Answering questions and completing tasks over large document collections often requires connecting evidence spread across multiple documents, such as a project's approval recorded in one, ....