ALFWorld

ALFWorld is a benchmark dataset used to evaluate agent systems' performance in executing tasks by leveraging textual skills while minimizing context overhead.

Papers using ALFWorld (32)

Reinforcement World Model Learning for LLM-based Agents2026Hindsight Credit Assignment for Long-Horizon LLM Agents2026Hera: Learning Long-Horizon Coordination for Device-Cloud Collaborative LLM Agents2026SELAUR: Self Evolving LLM Agent via Uncertainty-aware Rewards2026Group-in-Group Policy Optimization for LLM Agent Training2025 · 1 citesBlueprint First, Model Second: A Framework for Deterministic LLM Workflow2025SkillRL: Evolving Agents via Recursive Skill-Augmented Reinforcement Learning2026LatentSkill: From In-Context Textual Skills to In-Weight Latent Skills for LLM Agents2026Paying Less Generalization Tax: A Cross-Domain Generalization Study of RL Training for LLM Agents2026Hierarchical Reinforcement Learning with Augmented Step-Level Transitions for LLM Agents2026HiMAC: Hierarchical Macro-Micro Learning for Long-Horizon LLM Agents2026MemSkill: Learning and Evolving Memory Skills for Self-Evolving Agents2026Think Fast and Slow: Step-Level Cognitive Depth Adaptation for LLM Agents2026Reflecting with Two Voices: A Co-Adaptive Dual-Strategy Framework for LLM-Based Agent Decision Making2025Learning Hierarchical Procedural Memory for LLM Agents through Bayesian Selection and Contrastive Refinement2025SkillGen: Learning Domain Skills for In-Context Sequential Decision Making2025Reflect before Act: Proactive Error Correction in Language Models2025Memory-Driven Self-Improvement for Decision Making with Large Language Models2025Enhancing Decision-Making of Large Language Models via Actor-Critic2025GTR: Guided Thought Reinforcement Prevents Thought Collapse in RL-based VLM Agent Training2025WALL-E 2.0: World Alignment by NeuroSymbolic Learning improves World Model-based LLM Agents2025Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks2025Memp: Exploring Agent Procedural Memory2025Harnessing Uncertainty: Entropy-Modulated Policy Gradients for Long-Horizon LLM Agents2025Where LLM Agents Fail and How They can Learn From Failures2025GenEnv: Difficulty-Aligned Co-Evolution Between LLM Agents and Environment Simulators2025Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks2025Cache-Efficient Posterior Sampling for Reinforcement Learning with LLM-Derived Priors Across Discrete and Continuous Domains2025Structured Agent Distillation for Large Language Model2025Retrospex: Language Agent Meets Offline Reinforcement Learning Critic2025ReSpAct: Harmonizing Reasoning, Speaking, and Acting Towards Building Large Language Model-Based Conversational AI Agents2024 · 2 citesStateAct: Enhancing LLM Base Agents via Self-prompting and State-tracking2024