This repository contains the codebase for the paper Route to Reason: Adaptive Routing for LLM and Reasoning Strategy Selection.

An illustration of the RTR pipeline. We represent candidate LLMs and reasoning strategies with embeddings. For each query, RTR predicts the performance and output tokens of all combinations of LLM-strategy pairs, then routes to the optimal one considering both performance and cost.

1. Clone this repository:

   git clone <repo_url>
   cd rtr_test

2. Create and activate the conda environment:

   conda create -n rtr python=3.10
   conda activate rtr
   pip install -r requirements.txt

We use open-source math evaluation frameworks Qwen2.5-math and vLLM to generate our dataset.

Generate embeddings for models, strategies, and questions:

cd data
python ./data/gen_model_strategy_emb.py
python ./data/gen_question_emb.py

• encoder_model_path: Path to your sentence-transformer model used to encode questions, models, and strategies.

Train the model and run prediction:

python ./src/main.py

• --epochs: Number of training epochs (default: 100)
• --batch_size: Training batch size (default: 32)
• --lr: Learning rate (default: 1e-3)
• --cls_hidden_dim: Hidden layer dimension for the classifier (default: 768)
• --reg_hidden_dim: Hidden layer dimension for the regressor (default: 768)
• --patience: Patience for early stopping (default: 5)
• --encoder_model_path: Path to the sentence encoder model
• --predict: Whether to run prediction after training (default: False)