Author

Jose A. Barreiros

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Preprint Oct 2026

Sample-Efficient Reinforcement Learning for Policy Improvement via Flow-Reversed Action Anchors

This work introduces AnchorQ, which enables coarse-to-fine policy improvement through a structured, refinable latent action representation, and shows that anchors reduce identification complexity at the cost of support bias; particle refinement trades this bias against action-class complexity; and critic accuracy deter...

Carl Qi, Wei-Jun Dong, Xiao-Jie Wang et al. · 0 citations

#artificial intelligence Preprint Oct 2026

UniWALM: Unified World Action Language Model for Embodied Robot Control

Motivated by the human ability to integrate experience across language, perception, and action, we introduce UniWALM, a unified world-action-language model that learns video, action, and language in a single shared representation, where each modality is both a conditioning signal and a supervision target. We ask: can j...

Rahul Sajnani, D. Romeres, Ming-Yuan Zhang et al. · 0 citations

Jul 2026

Latent Memory Palace: Reasoning for Control as Autoregressive Variational Inference

This work shows that reasoning for control policies can emerge by organizing information in an autoregressive latent space reminiscent of a memory palace, where retrieval is iterative and adaptive.

Chu-Ning Zhu, E. Xu, Jose A. Barreiros et al. · arXiv.org · 0 citations