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.