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license: mit
language:
- en
metrics:
- accuracy
base_model:
- Qwen/Qwen2.5-3B-Instruct
pipeline_tag: reinforcement-learning
tags:
- agent
- reinforcement-learning
- routing
- tool-use
- reasoning
---
# ATLAS-RL: Reinforcement Learning for Adaptive Model–Tool Routing
This model is trained using the **ATLAS** framework proposed in the paper:
**ATLAS: Adaptive Tool–LLM Alignment and Synergistic Invocation via Reinforcement Learning**
📄 **Paper:** https://huggingface.co/papers/2601.03872
📄 **arXiv:** https://arxiv.org/abs/2601.03872
---
## Overview
ATLAS-RL is a reinforcement learning–trained routing model designed to coordinate large language models and external tools for complex reasoning tasks.
Instead of relying on static tool selection or predefined workflows, ATLAS learns a **policy that dynamically decides which model or tool to invoke** during multi-step reasoning. The policy is trained with reinforcement learning to optimize final task accuracy while maintaining efficient routing behavior.
Through RL optimization, the model learns to:
- select appropriate models for different problem types
- decide when external tools should be invoked
- coordinate multi-step reasoning across heterogeneous components
This enables more effective collaboration between language models and tools in tasks such as mathematical reasoning, coding, and scientific question answering.
---
## Experimental Results
The following tables summarize the performance of ATLAS compared to baseline routers. ATLAS (RL) demonstrates superior generalization, especially in Out-of-Distribution (OOD) scenarios.
### In-Distribution Performance
| Model | avg score |
| :--- | :--- |
| RouterDC | 53.4 |
| MLPRouter | 42.3 |
| BertRouter | 48.6 |
| **ATLAS (cluster)** | **63.5** |
### Out-of-Distribution Performance
| Model | avg score |
| :--- | :--- |
| RouterDC | 46.3 |
| MLPRouter | 39.3 |
| BertRouter | 43.6 |
| ATLAS (cluster) | 49.2 |
| **ATLAS (RL)** | **59.4** |
---
## Key Features
- 🔁 **RL-based Routing Policy** Learns routing strategies through reinforcement learning rather than heuristic rules.
- 🧠 **Adaptive Model–Tool Coordination** Dynamically determines which models or tools should be used during reasoning.
- ⚡ **Multi-step Decision Making** Supports iterative reasoning where multiple routing decisions may occur within a single query.
- 🌍 **Generalizable Routing Strategies** The learned policy transfers to unseen tasks without manual routing design.
---
## Reinforcement Learning Framework
ATLAS trains a routing policy using reinforcement learning to optimize decision making during reasoning.
At each step, the policy selects the next action (e.g., invoking a model or tool). The policy is optimized using reward signals that capture answer correctness, structured outputs, and routing efficiency.
The training objective encourages the model to discover **effective collaboration patterns between models and tools**, improving reasoning performance across diverse tasks.
---
## Citation
If you use this model or the ATLAS framework in your research, please cite:
```bibtex
@article{wu2026atlas,
title={Atlas: Orchestrating Heterogeneous Models and Tools for Multi-Domain Complex Reasoning},
author={Wu, Jinyang and Zhai, Guocheng and Jin, Ruihan and Yuan, Jiahao and Shen, Yuhao and Zhang, Shuai and Wen, Zhengqi and Tao, Jianhua},
journal={arXiv preprint arXiv:2601.03872},
year={2026}
}
``` |