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cs.CV2026
Causally Debiased Latent Action Model for Embodied Action-Conditioned World Models
Yufan Wei, Kun Zhou, Lingjun Mao +9
Action-conditioned world models (ACWMs) aim to simulate future observations conditioned on embodied actions. However, learning controllable ACWMs requires large-scale action-labele…
cs.LG2026
Learning to Explore with Parameter-Space Noise: A Deep Dive into Parameter-Space Noise for Reinforcement Learning with Verifiable Rewards
Bizhe Bai, Xinyue Wang, Peng Ye +1
Reinforcement Learning with Verifiable Rewards (RLVR) improves LLM reasoning, yet growing evidence indicates an exploration ceiling: it often reweights existing solution traces rat…
cs.LG2026
Transformer Is Inherently a Causal Learner
Xinyue Wang, Stephen Wang, Biwei Huang
We reveal that transformers trained in an autoregressive manner naturally encode time-delayed causal structures in their learned representations. When predicting future values in m…