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CST‑WM is a causally structured world model designed for embodied visual tracking, where a robot must keep a moving target visible and recover it after occlusion or drift. The model separates state into target‑evidence, robot, and observation branches, removing direct action‑to‑target‑evidence edges to prevent causal hallucination and instead letting actions influence evidence through robot motion and resulting views. Evaluated on EVT‑Bench, Habitat 3.0, and real‑world trials with a Unitree Go2 quadruped, CST‑WM outperforms reactive trackers and other world‑model baselines in following, distance control, safety, and re‑acquisition, achieving 20 of 30 successful real‑world recoveries versus 14 for TrackVLA.
arXiv:2608. 01130v1 Announce Type: new Abstract: A broad range of models face the mismatch where they are updated through trajectory losses but are evaluated by downstream task reward.
arXiv:2607. 23602v1 Announce Type: cross Abstract: Controllers based on sampling and latent world models assign a predicted terminal cost to each candidate action sequence, choose the minimum, execute its first action block, and replan.
arXiv:2510. 14807v3 Announce Type: replace Abstract: We revisit exploration collapse in reinforcement learning with verifiable rewards (RLVR), from the perspective of the \emph{candidate distribution} for next-token prediction.
arXiv:2606. 09115v1 Announce Type: new Abstract: Offline reinforcement learning (RL) offers a path to policy improvement from logged data alone, using historical returns or other measurable outcomes as world feedback.
The paper introduces ActObs, a supervised fine‑tuning method that, unlike standard approaches, also predicts environment observations in agent trajectories. While both ActObs and action‑only training perform similarly after initial fine‑tuning, ActObs diverges during subsequent reinforcement learning, yielding higher pass@k scores on several benchmarks and better cross‑domain task performance. The authors attribute this advantage to ActObs’s joint supervision, which preserves observation gradients and prevents the policy from over‑specializing on actions alone.