… AI-generated summary RL post-training of frontier language models is increasingly bottlenecked by autoregressive rollout generation , making rollout acceleration a central systems challenge. …
… One approach, reinforcement learning, rewards the model through trial and error and can work well, but it is costly because the model must repeatedly generate full back-and-forth conversations during training. …
… World models can generate concrete visual rollouts of possible futures, while MLLMs can reason abstractly over questions, goals, and rules. …
Papers arxiv:2606.02388 Policy and World Modeling Co-Training for Language Agents Published on Jun 1 Submitted by Ning Lu on Jun 2 HKUST Authors: Ning Lu , , , , , , , , , , , Abstract PaW is a co-training framework that combines policy learning and world modeling using on-policy reinforcement lear… …
… Then, we train a video diffusion model to generate these tokens using coarse-to-fine rollout . …
… GE-Sim 2.0 tops the public WorldArena leaderboard at only 2B parameters, outperforming both dedicated robotic world models and closed-source general video generators, and policies trained against its rollouts and rewards translate into measurable real-world gains, establishing GE-Sim 2.0 as a pract… …
… Could you share learning curves or metrics for the 120B model? The article mentions both vLLM and SGLang. Which engine is used for rollouts in the final experiments? …
Papers arxiv:2605.11550 The DAWN of World-Action Interactive Models Published on May 12 Submitted by LiangYao on May 14 COWARobot Authors: , Liang Yao , , , , , , , Abstract World-Action Interactive Models WAIMs jointly model scene evolution and actions through recursive refinement, enabling effect… …
… We then fine-tune the full decision--draft--reflect rollout with reinforcement learning RL , assigning terminal driving reward to the final post-edit trajectory and propagating policy-gradient credit through full-rollout transitions. …
… Interactive world models require controllable, causal, and low-latency rollout , which in practice demands a full pipeline spanning data construction, controllable fine-tuning, autoregressive training, few-step distillation, and streaming inference. …