• HRDL extends reward design to encode nuanced human preferences for long-horizon tasks. • L2HR translates natural language specifications into hierarchical reward signals for RL a
• TEB introduces task-aware exploration for visual RL with sparse rewards. • Uses predictive bisimulation metric to learn behaviorally grounded task representations. • Adds predict
• TPRU dataset addresses temporal and procedural gaps in multimodal LLMs, enabling richer embodied AI. • Comprised of robotic manipulation and GUI navigation scenes with 3 tasks: T
• Computer Science > Artificial Intelligence [Submitted on 18 Feb 2026] Title:EnterpriseGym Corecraft: Training Generalizable Agents on High-Fidelity RL Environments View PDF HTML
• Computer Science > Artificial Intelligence [Submitted on 18 Feb 2026] Title:EnterpriseGym Corecraft: Training Generalizable Agents on High-Fidelity RL Environments View PDF HTML
• Agentic RL extends LLM training beyond single-turn responses to full decision-making via environment interaction. • It collects on‑policy data, optimizing policies across multi‑s
• In this post, Iâll introduce a reinforcement learning (RL) algorithm based on an âalternativeâ paradigm: divide and conquer. • Unlike traditional methods, this algorithm is not b
• In this post, Iâll introduce a reinforcement learning (RL) algorithm based on an âalternativeâ paradigm: divide and conquer. • Unlike traditional methods, this algorithm is not b