arXiv Machine Learning
1d ago
Temporal Logic Guided Universal Task Representations for Reinforcement Learning
arXiv:2608. 15509v1 Announce Type: cross Abstract: Task guided agents demonstrate strong performance in a wide range of complex tasks.
Zero-Shot Instruction Following in RL via Structured LTL Representations
arXiv:2602. 14344v2 Announce Type: replace-cross Abstract: We study instruction following in multi-task reinforcement learning, where an agent must zero-shot execute novel tasks not seen during training.
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arXiv AI
2d ago
Semantically Labelled Automata for Multi-Task Reinforcement Learning with LTL Instructions
arXiv:2602. 06746v2 Announce Type: replace Abstract: We study multi-task reinforcement learning (RL), a setting in which an agent learns a single, universal policy capable of generalising to arbitrary, possibly unseen tasks.
arXiv AI
1d ago
Grounding LTL Tasks in Sub-Symbolic RL Environments for Zero-Shot Generalization
arXiv:2602. 09761v2 Announce Type: replace-cross Abstract: In this work we address the problem of training a Reinforcement Learning agent to follow multiple temporally-extended instructions expressed in Linear Temporal Logic in sub-symbolic environments.
Hugging Face Trending Papers
Jul 7
Progressive Reasoning with Primitive Correction for Compositional Zero-Shot Learning
Compositional Zero-Shot Learning (CZSL) aims to combine known attributes and objects as primitives for recognizing previously unseen attribute-object pairs. Prior works either predict attributes and objects independently, missing their strong contextual dependency, or use unidirectional conditional modeling (e.
arXiv Machine Learning
2d ago
hint$^2$: Hierarchical World Models for Inference-Time Temporal Logic Guidance
arXiv:2608. 13678v1 Announce Type: cross Abstract: A central goal of robot learning is to enable robots to execute rich instructions specified at runtime.
arXiv AI
Jun 4
Belief-Aware VLM Model for Human-like Reasoning
arXiv:2604. 09686v2 Announce Type: replace Abstract: Traditional neural network models for intent inference rely heavily on observable states and struggle to generalize across diverse tasks and dynamic environments.
arXiv AI
Jun 2
From Demonstrations to Rewards: Test-Time Prompt Optimization for VLM Reward Models
arXiv:2606. 00083v1 Announce Type: cross Abstract: Reinforcement learning relies on accurate reward functions, which are often hand-crafted or even unavailable in real-world applications, such as robotics.
arXiv AI
Jun 4
A Goal-Set Characterization of Task Composition in the Boolean Task Algebra
arXiv:2606. 04053v1 Announce Type: cross Abstract: The Boolean Task Algebra (BTA) provides a principled framework for zero-shot task composition in reinforcement learning by equipping goal-reaching tasks with Boolean operations.
arXiv Machine Learning
Jul 22
HERAKLES: Hierarchical Skill Compilation for Open-ended LLM Agents
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.
arXiv AI
Jul 20
RL-Struct: A Lightweight Reinforcement Learning Framework for Reliable Structured Output in LLMs
arXiv:2512. 00319v3 Announce Type: replace Abstract: The Structure Gap between probabilistic LLM generation and deterministic schema requirements hinders automated workflows.
arXiv AI
Aug 5
Hybrid LLM-Augmented Reinforcement Learning Agents for Complex Sequential Decision 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.
arXiv Machine Learning
Jul 1
ViTL: Temporal Logic-Guided Zero-Shot Natural Language Navigation via Vision-Language Models
arXiv:2606. 30696v1 Announce Type: cross Abstract: Enabling robots to follow natural language commands to complete zero-shot long-horizon tasks remains challenging.