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ExTra introduces exploratory trajectory optimization for language model reinforcement learning, combining novelty rewards and entropy-guided prefix regeneration to improve both single-sample accuracy and inference-time coverage on mathematical reasoning benchmarks.
This paper proposes a CKM-driven framework for multi-UAV trajectory planning in urban inspection, using diffusion models to reconstruct high-fidelity channel quality maps and a graph attention network with soft actor-critic algorithm for communication-aware path planning.
This paper introduces CAPR (Cached-Amortized Path Refinement), a reinforcement learning algorithm for diffusion large language models that extracts tree-like supervision signals from the denoising trace without the compute cost of full tree rollouts. CAPR achieves state-of-the-art performance on reasoning benchmarks like GSM8K, Math500, Sudoku, and Countdown at roughly 0.75x the cost of flat rollouts.
This paper introduces FATE, an on-policy framework that leverages failure trajectories to enhance the safety and performance of tool-using LLM agents through self-evolution and Pareto-aware optimization.
OpenAI proposes POLO (Plan Online, Learn Offline), a framework combining model-based control with value function learning and coordinated exploration to enable efficient learning on complex control tasks like humanoid locomotion and dexterous manipulation with minimal real-world experience.
OpenAI introduces a method for learning complex nonlinear system dynamics using deep generative models over temporal segments, enabling stable long-horizon predictions and differentiable trajectory optimization for model-based control.