Retrieval-Augmented LLM Agents: Learning to Learn from Experience
cs.AI, cs.CL
Submitted: 2026-03-18
Updated: 2026-08-31
Comments: Accepted at EMNLP 2026 - Main Conference
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- FireAct: Toward Language Agent Fine-tuning
- Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory
- Unleashing Embodied Task Planning Ability in LLMs via Reinforcement Learning
- Memory in the Age of AI Agents
- Grokking as the Transition from Lazy to Rich Training Dynamics
- Imagine-then-Plan: Agent Learning from Adaptive Lookahead with World Models
- MemGPT: Towards LLMs as Operating Systems
- Qwen2.5 Technical Report
- Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
- Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG
- Gemma 3 Technical Report
- A Practitioner's Guide to Multi-turn Agentic Reinforcement Learning
- RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning
- Evo-Memory: Benchmarking LLM Agent Test-time Learning with Self-Evolving Memory
- From Human Memory to AI Memory: A Survey on Memory Mechanisms in the Era of LLMs
- SkillRL: Evolving Agents via Recursive Skill-Augmented Reinforcement Learning
- Qwen2.5-1M Technical Report
- Quantifying and Improving the Robustness of Retrieval-Augmented Language Models Against Spurious Features in Grounding Data
- Agentic Memory: Learning Unified Long-Term and Short-Term Memory Management for Large Language Model Agents
- Can World Models Benefit VLMs for World Dynamics?
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