Behavior Knowledge Merge In Reinforced Agentic

---
name: behavior-knowledge-merge-in-reinforced-agentic
title: "Behavior Knowledge Merge in Reinforced Agentic Models"
version: 0.0.2
engine: skillxiv-v0.0.2-claude-opus-4.6
license: MIT
url: "https://arxiv.org/abs/2601.13572"
keywords: [Agent, Model]
description: "Reinforcement learning (RL) is central to post-training, particularly for agentic models that require specialized reasoning behaviors. In this setting, model merging offers a practical mechanism for integrating multiple RL-trained agents from different tasks into a single generalist model. However, existing merging methods are designed for supervised fine-tuning (SFT), and they are suboptimal to preserve task-specific capabilities on RL-trained agentic models. The root is a task-vector mismatch ..."
---

## Overview

This skill covers research on behavior knowledge merge in reinforced agentic models. It addresses important challenges in agent development and evaluation.

## Key Insights

The paper provides:
- Novel approaches or frameworks for agent systems
- Empirical evaluation results and benchmarks
- Generalizable principles for practitioners

## When to Use

Use this skill when working on:
- Agent-based systems and applications
- Autonomous reasoning and planning
- Agent performance evaluation and improvement

## When NOT to Use

- For non-agent-related tasks
- When seeking implementation code (consult the paper)

## Resources

- ArXiv Abstract: https://arxiv.org/abs/2601.13572
- Full PDF: https://arxiv.org/pdf/2601.13572
- HTML: https://arxiv.org/html/2601.13572

Refer to the original paper for complete technical details, methodology, and experimental protocols.