How I Study AI - Learn AI Papers & Lectures the Easy Way

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

Imagine-then-Plan: Agent Learning from Adaptive Lookahead with World Models

Intermediate

Youwei Liu, Jian Wang et al.Jan 13arXiv

Agents often act like tourists without a map: they react to what they see now and miss long-term consequences.

#Imagine-then-Plan#world models#adaptive lookahead

Papers3

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

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

Imagine-then-Plan: Agent Learning from Adaptive Lookahead with World Models