The paper reviews hierarchical reinforcement learning (HRL) as a method for enabling agents to explore, plan, and learn in complex, open-ended environments by uncovering temporal structure in experience streams. It discusses the unclear definition of what makes a structure useful, the benefits of HRL for decision‑making challenges, and its impact on AI agent performance trade‑offs. The authors survey various HRL methods—from online learning to offline datasets and large language model integration—and outline the challenges and suitable domains for temporal structure discovery.
arXiv:2606. 10507v1 Announce Type: new Abstract: While Large Language Models (LLMs) have demonstrated strong capabilities as autonomous agents across a wide range of tasks, their performance often degrades in multi-turn long-horizon agentic tasks.
arXiv:2608. 03502v1 Announce Type: new Abstract: Large Language Models (LLMs) have recently shown strong capabilities in reasoning, planning, and tool-use, enabling new forms of autonomous agents.
The paper introduces Q-Shaped Options (QSO), a hierarchical reinforcement learning framework that learns action abstractions between consecutive levels using shared encoders shaped by Q functions. QSO employs distinct state-value functions, Q functions, and policies at each hierarchy level, encouraging the low-level Q function to preserve distinctions needed for optimal control while the high-level Q function discards unnecessary distinctions. Experiments on offline goal-conditioned locomotion and manipulation tasks show that QSO learns semantically meaningful option spaces and outperforms baselines, achieving non-zero performance where other algorithms fail.
arXiv:2607. 27973v1 Announce Type: new Abstract: Recently, Reinforcement Learning (RL) has emerged as a crucial paradigm for the post-training of Large Language Model (LLM) agents.
arXiv:2606. 03698v1 Announce Type: new Abstract: A central goal of large language model (LLM) research is to build agentic systems that can plan, act, and adapt through sustained interaction with dynamic environments.
arXiv:2507. 23604v2 Announce Type: replace Abstract: Decentralized Multi-Agent Reinforcement Learning (MARL) methods allow for learning scalable multi-agent policies, but suffer from partial observability and induced non-stationarity.
arXiv:2508. 14751v2 Announce Type: replace Abstract: We study goal-conditioned reinforcement learning in partially observable environments with sparse rewards and large, structured goal spaces.
TrajEvolve introduces a trajectory evolution paradigm with hindsight credit assignment to improve reinforcement learning for large language models on long-horizon tasks. It refines low-quality steps in historical trajectories to expose the policy to high-quality compositions that are rarely sampled, and reformulates step-level continuous credit assignment as a binary classification problem to identify redundant steps. Experiments on two datasets show that this approach outperforms existing methods.
arXiv:2606. 17680v1 Announce Type: new Abstract: Reinforcement learning (RL) has emerged as a powerful paradigm for training Large Language Models (LLMs) as agents.
arXiv:2505. 13372v2 Announce Type: replace Abstract: Recent work investigated the use of Reinforcement Learning (RL) for the synthesis of heuristic guidance to improve the performance of temporal planners when a domain is fixed and a set of training problems (not plans) is given.
arXiv:2602. 05031v2 Announce Type: replace Abstract: Planning with a learned model remains a key challenge in model-based reinforcement learning (RL).
StraTA introduces Strategic Trajectory Abstraction, a framework that samples a compact strategy from the initial task state and conditions subsequent actions on that strategy, training strategy generation and action execution jointly with a hierarchical GRPO-style rollout design. The method enhances exploration and credit assignment over long horizons by incorporating diverse strategy rollouts and critical self-judgment. Experiments on ALFWorld, WebShop, and SciWorld demonstrate that StraTA consistently improves sample efficiency and final performance, achieving success rates of 93.1% on ALFWorld, 84.2% on WebShop, and a 63.5% overall score on SciWorld, surpassing frontier closed‑source models.