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Exploratory Memory-Augmented LLM Agent via Hybrid On- and Off-Policy Optimization

Intermediate

Zeyuan Liu, Jeonghye Kim et al.Feb 26arXiv

This paper teaches a language-model agent to explore smarter by combining two ways of learning (on-policy and off-policy) with a simple, self-written memory.

#EMPO#memory-augmented agents#on-policy learning

Spark: Strategic Policy-Aware Exploration via Dynamic Branching for Long-Horizon Agentic Learning

Intermediate

Jinyang Wu, Shuo Yang et al.Jan 28arXiv

SPARK is a new way to train AI agents that saves compute by exploring more only at the most important moments.

#SPARK#dynamic branching#strategic exploration

Paying Less Generalization Tax: A Cross-Domain Generalization Study of RL Training for LLM Agents

Beginner

Zhihan Liu, Lin Guan et al.Jan 26arXiv

LLM agents are usually trained in a few worlds but asked to work in many different, unseen worlds, which often hurts their performance.

#cross-domain generalization#state information richness#planning complexity

From Word to World: Can Large Language Models be Implicit Text-based World Models?

Intermediate

Yixia Li, Hongru Wang et al.Dec 21arXiv

This paper asks if large language models (LLMs) can act like "world models" that predict what happens next in text-based environments, not just the next word in a sentence.

#world models#next-state prediction#text-based environments