ATLAS-RL

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

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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.

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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

Out-of-Distribution Performance

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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.

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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.

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Citation

If you use this model or the ATLAS framework in your research, please cite:

@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}
}