arXiv Machine Learning
Divergent Paths: Learning an Outcome-Predictive Representation of LLM Agent Trajectories
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arXiv AI
Sep 30
Beyond a single latent space: a dual-latent world model for long-horizon planning
The paper introduces the Dual-Latent World Model (Dual-WM), which separates local execution and long-range planning into distinct latent spaces and dynamics models. A new learning method, Long-Horizon Representation Learning with Weighted Rollout (LoRe), supervises predictions at both levels using exponential horizon weights. Experiments on five goal-conditioned visual control tasks show that Dual-WM improves success rates over strong baselines, especially at longer horizons.
arXiv AI
4d ago
World Action Critic (WAC) Model: Joint Foresight and Criticism for Policy Learning
arXiv:2610.22825v1 Announce Type: new Abstract: World models have shown considerable promise in sequential decision-making by anticipating the consequences of actions and providing predictive foresig...
arXiv Computer Vision
Sep 7
Persistent Robot World Models: Stabilizing Multi-Step Rollouts via Reinforcement Learning
The paper introduces a reinforcement learning post‑training scheme that trains robot world models on their own autoregressive rollouts, using a contrastive RL objective adapted from diffusion models. It also proposes a training protocol that compares multiple variable‑length futures, a multi‑view visual fidelity reward, and demonstrates state‑of‑the‑art rollout fidelity on the DROID dataset, outperforming baselines on LPIPS, SSIM, and human preference tests.
arXiv Machine Learning
Jul 31
TAPO: Transition-Aware Policy Optimization for LLM Agents
arXiv:2607. 27973v1 Announce Type: new Abstract: Recently, Reinforcement Learning (RL) has emerged as a crucial paradigm for the post-training of Large Language Model (LLM) agents.
arXiv AI
Aug 17
Traj-LeWM: Path-Aware World-Model Planning via Latent Trajectory Cost
arXiv:2608. 14125v1 Announce Type: new Abstract: LeWM is a lightweight visual world model that learns latent dynamics end-to-end from pixels and ranks candidate action sequences by the distance between their predicted endpoints and the goal.
arXiv AI
2d ago
VPFlow: Value-Predictive Planning with Imperfect World Models for Offline Goal Reaching
arXiv:2610.27280v1 Announce Type: cross Abstract: Offline goal-conditioned reinforcement learning enables skill acquisition by reaching any goal from any state without dense rewards. Model-based meth...