reinforcement-learning

Weekly Paper Notes — one of the top picks from the 2026-07-25 CS paper digest. Area: AI / ML (Agent Training). Authors: Xiao Yu, Baolin Peng, Ruize Xu et al. arXiv: 2607.21557 · PDF TL;DR Modern agents are shaped less by their base model and more by the harness wrapped around it — Claude Code, Codex, OpenClaw, GUI-use scaffolds. But those harnesses are stateful, multi-process, and full of tool orchestration, which means existing open SFT/RL stacks (veRL, TRL, OpenRLHF) can’t natively express a rollout inside one....

Weekly Paper Notes — one of the top picks from the 2026-06-27 CS paper digest. Area: AI / ML. Authors: Nicklas Hansen, Xiaolong Wang (UC San Diego) arXiv: 2606.27326 · PDF · Interactive TL;DR Generative action-conditional world models produce visually fluent rollouts that drift from the true dynamics — the “fluent but wrong” failure mode familiar from LLMs, now in video. Hansen and Wang argue this is fundamentally a data coverage problem: hallucinations concentrate in regions of state-action space the training set under-samples, and cheap data-centric signals computed at inference time can both detect and prevent them....