Expected Reward Prediction, with Applications to Model Routing
arXiv:2603.20217v1 Announce Type: new Abstract: Reward models are a standard tool to score responses from LLMs. Reward models are built to rank responses to a fixed prompt sampled from a single model, for example to choose the best of n sampled responses. In this paper, we study whether scores from response-level reward models lifted to score a model's suitability for a prompt, prior to seeing responses from that model. Specifically, we show that it is straightforward to predict the expected reward that an LLM would earn from the reward model under repeated sampling. Further, we show that these expected reward predictions are precise and discriminative enough to support an application to a model routing protocol that routes prompts to models at inference time to maximize reward while controlling computational cost. We demonstrate the performance of this routing procedure on the open-perfectblend dataset, using a model pool composed of Llama3.1-Instruct 8B/70B, Gemma2-IT 9B/27B, and Ge
arXiv:2603.20217v1 Announce Type: new Abstract: Reward models are a standard tool to score responses from LLMs. Reward models are built to rank responses to a fixed prompt sampled from a single model, for example to choose the best of n sampled responses. In this paper, we study whether scores from response-level reward models lifted to score a model's suitability for a prompt, prior to seeing responses from that model. Specifically, we show that it is straightforward to predict the expected reward that an LLM would earn from the reward model under repeated sampling. Further, we show that these expected reward predictions are precise and discriminative enough to support an application to a model routing protocol that routes prompts to models at inference time to maximize reward while controlling computational cost. We demonstrate the performance of this routing procedure on the open-perfectblend dataset, using a model pool composed of Llama3.1-Instruct 8B/70B, Gemma2-IT 9B/27B, and Gemma1-IT 7B models. Our simple expected reward prediction--based routing (ERP) outperforms baselines that route prompts to models with the best average performance within each prompt's category, and explains the success of more complex routing protocols that implicitly estimate an expected reward. Our approach has the added advantage of being trivially extensible as new models are added to the pool.
Executive Summary
This article presents a novel approach to model routing in large language models (LLMs), leveraging expected reward prediction to optimize routing decisions. By predicting the expected reward of an LLM prior to response generation, the authors demonstrate improved routing performance compared to traditional baselines. The proposed method, expected reward prediction--based routing (ERP), is shown to outperform average performance-based routing and explain the success of more complex routing protocols. The authors' use of a simple and extensible approach highlights the potential of this method in real-world applications. The study's findings and methodology provide valuable insights into the development of more efficient and effective LLM routing protocols.
Key Points
- ▸ Expected reward prediction is used to optimize model routing in LLMs.
- ▸ The proposed ERP method outperforms traditional baselines and complex routing protocols.
- ▸ ERP is simple, extensible, and computationally efficient.
Merits
Strength in Predictive Accuracy
The authors demonstrate high predictive accuracy of expected reward predictions, enabling effective routing decisions.
Improved Routing Performance
ERP outperforms traditional baselines and complex routing protocols, indicating its potential in real-world applications.
Ease of Extension
The proposed method is trivially extensible as new models are added to the pool, making it a scalable solution.
Demerits
Assumes Access to Multiple Models
The proposed ERP method requires access to a pool of multiple LLMs, which may not be feasible in all scenarios.
Potential Overfitting
The authors acknowledge the risk of overfitting in the expected reward prediction model, suggesting the need for careful model selection and hyperparameter tuning.
Expert Commentary
The proposed ERP method presents a significant advancement in the development of efficient model routing protocols for LLMs. By leveraging expected reward prediction, the authors demonstrate improved routing performance and scalability. The study's findings and methodology provide valuable insights into the challenges and opportunities of large-scale AI research. However, the authors' assumption of access to multiple models and the risk of overfitting highlight the need for careful consideration of the practical and policy implications of this work. Future studies should explore the generalizability of ERP and its potential applications in various domains.
Recommendations
- ✓ Further research is needed to explore the generalizability of the proposed ERP method and its potential applications in various domains.
- ✓ Developers and policymakers should consider the practical and policy implications of the study's findings, including the importance of access to multiple models and the risk of overfitting.
Sources
Original: arXiv - cs.CL
