…models often struggle in this setting. Trained primarily for short-horizon continuation, they treat long sequences as extended single shots, inevitably suffering from motion stagnation and semantic drift during long rollouts. To…
…Generated by Qwen/Qwen2.5-Coder-32B-Instruct Training computer-use agents (CUAs) -- models that interact with graphical desktops through screenshots and keyboard/mouse actions -- requires large-scale, diverse trajectory data collected…
…Yuxiang Ji , , , , , , , , Abstract Agentic reinforcement learning for large language models leverages action data from human interactions as reference guidance to improve exploration and reduce dependence on costly supervised fine-tuning. AI-generated…
…Xiaobo Wang , , , , , Abstract SAVE framework improves reward model training by using value functions to grade on-policy responses and update models through contrastive objectives. Generated by Qwen/Qwen2.5-Coder-32B-Instruct…
…a training rollout is generated on demand by a controllable generator-verifier program that samples a fresh latent visual state , renders an image, asks a question, and exactly verifies the answer. A…
…An Advanced World Action Model for Robot Control (2026) Learning Human-Intention Priors from Large-Scale Human Demonstrations for Robotic Manipulation (2026) Being-H0.7: A Latent World-Action Model from Egocentric…
…Any-Step Video Diffusion Model with On-Policy Flow Map Distillation Published on May 13 Submitted by taesiri on May 14 #3 Paper of the day NVIDIA Authors: , Guian Fang , , , , Han Cai…
…The model is trained on approximately 27{,}300 hours of real-robot teleoperation, UMI-style interaction, egocentric human videos, and rollout or failure trajectories using modality-specific supervision masks. At inference time…
…We then close the loop with the model itself: an online process-reward co-learning loop , in which an open-source agent's rollouts through the refining harness are relabeled by a…
…for diffusion models based on Online Policy Distillation (OPD). DiffusionOPD first trains task-specific teachers independently, then distills their capabilities into a unified student along the student own rollout trajectories. This decouples…