ADu2021/behavior-knowledge-merge-in-reinforced-agentic
skills/skillxiv-v0.0.2-claude-opus-4.6/behavior-knowledge-merge-in-reinforced-agentic/SKILL.md
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 ...
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SKILL.md
- 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.
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