Industrial actor--critic methods usually represent continuous actions as anonymous numerical coordinates. They must therefore learn from limited interactions which process variables each action affect...
The paper introduces PolicyLift, a three‑stage framework that improves decision policies using observational data for complex, structured actions such as text or images. Stage 1 learns discrete action codes that capture high‑level strategies, Stage 2 trains a context‑dependent policy over these codes, and Stage 3 generates full actions conditioned on the learned codes. The authors provide theoretical guarantees that improving the code‑level policy leads to improvement in the original action space and demonstrate consistent policy gains in synthetic and real‑world text generation experiments.
By Jonas Schweisthal, Dennis Frauen, Stefan Feuerriegel, Emma Brunskill
The paper introduces Agentic In-Context Learning (Agentic ICL), a framework where fixed‑parameter Transformers learn from interactions that shape the context they receive. It models an interactive learner with state, update, and decision rules, covering active learning, bandits, and online reinforcement learning, and demonstrates that causal Transformers can approximate these algorithms over a finite horizon. Experiments show that Transformers can learn to execute recursions and that different reward signals produce distinct decision policies, with alignment between process supervision and outcome‑optimal actions influencing learned behavior.
By Shiguang Wu, Yaqing Wang, Quanming Yao
Vision-Language-Action (VLA) models can turn multimodal context into robot actions, but their action decoders are still trained largely by behavior cloning. This supervises which motor command was dem...
The paper introduces the Agent-Editing World Model (AEWM), a new approach that models how reasoning and actions influence future task progress instead of simulating tool responses. AEWM includes an Action Judge that classifies decisions as Critical, Exploratory, or Noisy, and a State Revision mechanism that edits noisy reasoning–action continuations from the same observed history. The integrated system, EditAct, directly updates the underlying state during real execution, leading to significant performance gains across multiple benchmarks and agent backbones.
By Shuang Sun, Guoxin Chen, Fanzhe Meng, Jia Deng, Huatong Song, Jinhao Jiang, Wayne Xin Zhao, Hongteng Xu, Ji-Rong Wen
The paper introduces Intention Distillation (INDI), a method that injects behavior-level intent into Vision‑Language‑Action (VLA) model decoders by leveraging a frozen teacher vision‑language model to interpret demonstrations. During training, the teacher processes the current observation, instruction, coarse action summary, and execution video, producing a multimodal intent representation that the VLA decoder uses alongside trajectory and execution features to predict actions. Experiments on SimplerEnv‑Bridge, RoboCasa Kitchen, and real‑world tasks show that INDI consistently improves success rates, especially on longer‑horizon tasks, demonstrating that explicit modeling of semantic intent benefits action decoders.
By Sangoh Lee, Sangwoo Mo, Wook-Shin Han