Agentic R Learning To Retrieve For Agentic Search

---
name: agentic-r-learning-to-retrieve-for-agentic-search
title: "Agentic-R: Learning to Retrieve for Agentic Search"
version: 0.0.2
engine: skillxiv-v0.0.2-claude-opus-4.6
license: MIT
url: "https://arxiv.org/abs/2601.11888"
keywords: [Agent, Learning]
description: "Agentic search has recently emerged as a powerful paradigm, where an agent interleaves multi-step reasoning with on-demand retrieval to solve complex questions. Despite its success, how to design a retriever for agentic search remains largely underexplored. Existing search agents typically rely on similarity-based retrievers, while similar passages are not always useful for final answer generation. In this paper, we propose a novel retriever training framework tailored for agentic search. Unlike..."
---

## Problem

Agentic-R addresses key challenges in autonomous agent development. This paper provides solutions for evaluating, building, or improving agent systems.

## Key Approach

The paper introduces a novel framework, methodology, or benchmark for agentic-r. The core contributions include:

1. Systematic framework or benchmark for agent evaluation and development
2. Empirical findings on agent performance, efficiency, or capabilities  
3. Generalizable principles applicable across domains

## When to Use

Use this skill when you need to:
- Evaluate or benchmark autonomous agent systems
- Understand best practices in agent design and evaluation
- Learn empirical results on agent performance
- Improve agent efficiency, reasoning, or capabilities

## When NOT to Use

- For non-agent-related tasks
- When seeking quick implementation code (see the paper for details)
- For general knowledge unrelated to autonomous agents

## Resources

- ArXiv Abstract: https://arxiv.org/abs/2601.11888
- Full PDF: https://arxiv.org/pdf/2601.11888
- HTML Version: https://arxiv.org/html/2601.11888

See the paper for comprehensive methodology, experimental protocols, benchmarks, and implementation details.