# Behavior Knowledge Merge In Reinforced Agentic

> 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 ...

- Skill: `adu2021/behavior-knowledge-merge-in-reinforced-agentic` (Agent Skill)
- Install (CLI): `npx skillmds@latest add adu2021/behavior-knowledge-merge-in-reinforced-agentic`
- Raw SKILL.md: https://api.skillmd.com/api/skills/adu2021/behavior-knowledge-merge-in-reinforced-agentic/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- License: MIT
- Author: adu2021 (https://skillmd.com/u/adu2021)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/adu2021/behavior-knowledge-merge-in-reinforced-agentic

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## 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.