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This paper proposes InK, a neurosymbolic hierarchical reinforcement learning approach that uses incremental knowledge for symbolic planning and reward-shaped low-level neural modules, achieving improved sample efficiency in navigation tasks.
HOBA proposes a hierarchical reinforcement learning framework for online advertising that uses a large language model for hyperparameter inference, a SARSA agent for expert model selection, and a dynamic expert pool for bid execution, achieving a +3.6% improvement in a large-scale A/B test.
This paper proposes NFTR, a method for offline goal-conditioned reinforcement learning that uses normalizing flows for subgoal policies and a triangle-slack reweighting to address optimistic bias and mode collapse in hierarchical implicit Q-learning.
This paper proposes a hierarchical multi-agent reinforcement learning framework that enforces hard safety constraints via a constraint manifold at the low level while enabling effective coordination through high-level policy learning, providing theoretical safety guarantees and achieving near-perfect safety rates with good generalization.
This paper introduces CARL, a method for offline hierarchical reinforcement learning that exploits local dynamics regularity to learn reusable skills. The approach clusters state-goal pairs requiring similar action sequences, enabling more effective skill reuse and improved performance on complex humanoid tasks.
Introduces Inquisitive Conversational Agents (ICAs) for proactive information extraction in legal dialogue, proposing a Dual Hierarchical Reinforcement Learning framework that learns when and how to ask probing questions, evaluated on U.S. Supreme Court oral arguments.
StraTA proposes strategic trajectory abstraction for long-horizon LLM agents, using hierarchical GRPO-style rollout with diverse strategy sampling and critical self-judgment to improve sample efficiency and final performance over frontier models and prior RL baselines.
Agent Lightning introduces a flexible reinforcement learning framework for training large language models in AI agents, achieving decoupling between agent execution and training to handle complex interactions.