…AI-generated summary Reinforcement Learning has significantly advanced the reasoning capabilities of Multimodal Large Language Models (MLLMs), yet the resulting policies remain brittle against real-world visual degradation s such as blur…
…We then train a family of capable Claw-style models, termed ClawGym-Agents , through supervised fine-tuning on black-box rollout trajectories , and further explore reinforcement learning via a lightweight pipeline that…
…rollout tokens cumulatively. View arXiv page View PDF Add to collection Community ESPO (Early-Stopping Proximal Policy Optimization) tackles a key waste in RL training of reasoning LLMs: when a model errs…
…On the system side, an asynchronous design overlaps rollout , gradient update , and policy refresh across iterations, paired with two web-agent-specific adaptations, namely an everlasting rollout pool and lightweight screenshot handling…
…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…
…GRPO-style rollout, enhanced by diverse strategy sampling and critical self-judgment. Does it work? StraTA improves both sample efficiency and final performance, outperforming both frontier closed-source models and prior RL…
…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? If different engines are…
…Across a range of self-distillation settings and external teacher model s, we observe that distillation guidance exhibits substantially higher alignment with the ideal on incorrect rollouts than on correct ones, where…
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…
…On top of this harness, On-policy Data Evolution (ODE) runs a closed-loop data generator that refines itself across rounds from rollouts of the policy being trained. This per-round refinement…
