# Capability Instruction Tuning: A New Paradigm for Dynamic LLM Routing

arXiv: https://arxiv.org/abs/2502.17282 · HTML: https://mlsanity.com/paper/2502.17282/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 44 references that are in the map.
- First posted on arXiv 24 Feb 2025.

## Builds on (most cited first, 30 of 32)

- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://mlsanity.com/paper/2403.05530/) (2024)
- [BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions](https://mlsanity.com/paper/1905.10044/) (2019)
- [MMBench: Is Your Multi-modal Model an All-around Player?](https://mlsanity.com/paper/2307.06281/) (2023)
- [Microsoft COCO Captions: Data Collection and Evaluation Server](https://mlsanity.com/paper/1504.00325/) (2015)
- [ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools](https://mlsanity.com/paper/2406.12793/) (2024)
- [GLM: General Language Model Pretraining with Autoregressive Blank Infilling](https://mlsanity.com/paper/2103.10360/) (2021)
- [ShareGPT4V: Improving Large Multi-Modal Models with Better Captions](https://mlsanity.com/paper/2311.12793/) (2023)
- [A Diagram Is Worth A Dozen Images](https://mlsanity.com/paper/1603.07396/) (2016)
- [Yi: Open Foundation Models by 01.AI](https://mlsanity.com/paper/2403.04652/) (2024)
- [Hybrid LLM: Cost-Efficient and Quality-Aware Query Routing](https://mlsanity.com/paper/2404.14618/) (2024)
- [RouterBench: A Benchmark for Multi-LLM Routing System](https://mlsanity.com/paper/2403.12031/) (2024)
- [mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval](https://mlsanity.com/paper/2407.19669/) (2024)
- [LEEP: A New Measure to Evaluate Transferability of Learned Representations](https://mlsanity.com/paper/2002.12462/) (2020)
- [LogME: Practical Assessment of Pre-trained Models for Transfer Learning](https://mlsanity.com/paper/2102.11005/) (2021)
- [Large Language Model Routing with Benchmark Datasets](https://mlsanity.com/paper/2309.15789/) (2023)
- [Transferability and Hardness of Supervised Classification Tasks](https://mlsanity.com/paper/1908.08142/) (2019)
- [An Information-Theoretic Approach to Transferability in Task Transfer Learning](https://mlsanity.com/paper/2212.10082/) (2022)
- [Think you have Solved Direct-Answer Question Answering? Try ARC-DA, the Direct-Answer AI2 Reasoning Challenge](https://mlsanity.com/paper/2102.03315/) (2021)
- [OTCE: A Transferability Metric for Cross-Domain Cross-Task Representations](https://mlsanity.com/paper/2103.13843/) (2021)
- [Ranking Neural Checkpoints](https://mlsanity.com/paper/2011.11200/) (2020)
- [Harnessing the Power of Multiple Minds: Lessons Learned from LLM Routing](https://mlsanity.com/paper/2405.00467/) (2024)
- [A linearized framework and a new benchmark for model selection for fine-tuning](https://mlsanity.com/paper/2102.00084/) (2021)
- [PACTran: PAC-Bayesian Metrics for Estimating the Transferability of Pretrained Models to Classification Tasks](https://mlsanity.com/paper/2203.05126/) (2022)
- [Ranking and Tuning Pre-trained Models: A New Paradigm for Exploiting Model Hubs](https://mlsanity.com/paper/2110.10545/) (2021)
- [Blending Is All You Need: Cheaper, Better Alternative to Trillion-Parameters LLM](https://mlsanity.com/paper/2401.02994/) (2024)

## Built on by (most cited first, 25 of 25)

- [RouterEval: A Comprehensive Benchmark for Routing LLMs to Explore Model-level Scaling Up in LLMs](https://mlsanity.com/paper/2503.10657/) (2025)
- [The Avengers: A Simple Recipe for Uniting Smaller Language Models to Challenge Proprietary Giants](https://mlsanity.com/paper/2505.19797/) (2025)
- [LLMRouterBench: A Massive Benchmark and Unified Framework for LLM Routing](https://mlsanity.com/paper/2601.07206/) (2026)
- [OmniRouter: Budget and Performance Controllable Multi-LLM Routing](https://mlsanity.com/paper/2502.20576/) (2025)
- [ICL-Router: In-Context Learned Model Representations for LLM Routing](https://mlsanity.com/paper/2510.09719/) (2025)
- [RouterArena: An Open Platform for Comprehensive Comparison of LLM Routers](https://mlsanity.com/paper/2510.00202/) (2025)
- [EvoRoute: Experience-Driven Self-Routing LLM Agent Systems](https://mlsanity.com/paper/2601.02695/) (2026)
- [MMR-Bench: A Comprehensive Benchmark for Multimodal LLM Routing](https://mlsanity.com/paper/2601.17814/) (2026)
- [Mixture of Thoughts: Learning to Aggregate What Experts Think, Not Just What They Say](https://mlsanity.com/paper/2509.21164/) (2025)
- [Learning Compact Representations of LLM Abilities via Item Response Theory](https://mlsanity.com/paper/2510.00844/) (2025)
- [When Routing Collapses: On the Degenerate Convergence of LLM Routers](https://mlsanity.com/paper/2602.03478/) (2026)
- [Task-Aware LLM Routing with Multi-Level Task-Profile-Guided Data Synthesis for Cold-Start Scenarios](https://mlsanity.com/paper/2604.09377/) (2026)
- [INFERENCEDYNAMICS: Efficient Routing Across LLMs through Structured Capability and Knowledge Profiling](https://mlsanity.com/paper/2505.16303/) (2025)
- [Optimal-Agent-Selection: State-Aware Routing Framework for Efficient Multi-Agent Collaboration](https://mlsanity.com/paper/2511.02200/) (2025)
- [Orchestrating Intelligence: Confidence-Aware Routing for Efficient Multi-Agent Collaboration across Multi-Scale Models](https://mlsanity.com/paper/2601.04861/) (2026)
- [Chimera: Latency- and Performance-Aware Multi-agent Serving for Heterogeneous LLMs](https://mlsanity.com/paper/2603.22206/) (2026)
- [Near-Optimal Online Deployment and Routing for Streaming LLMs](https://mlsanity.com/paper/2506.17254/) (2025)
- [One-Embedding-Fits-All: Efficient Zero-Shot Time Series Forecasting by a Model Zoo](https://mlsanity.com/paper/2509.04208/) (2025)
- [Breaking Model Lock-in: Cost-Efficient Zero-Shot LLM Routing via a Universal Latent Space](https://mlsanity.com/paper/2601.06220/) (2026)
- [Towards Fair and Comprehensive Evaluation of Routers in Collaborative LLM Systems](https://mlsanity.com/paper/2602.11877/) (2026)
- [Beyond Gemini-3-Pro: Revisiting LLM Routing and Aggregation at Scale](https://mlsanity.com/paper/2601.01330/) (2026)
- [RouteJudge: An Open Platform for Reproducible and Preference-Aware LLM Routing](https://mlsanity.com/paper/2606.18774/) (2026)
- [A Scalable Multi-LLM Collaboration System with Retrieval-based Selection and Exploration-Exploitation-Driven Enhancement](https://mlsanity.com/paper/2507.14200/) (2025)
- [Efficient Multi-Model Orchestration for Self-Hosted Large Language Models](https://mlsanity.com/paper/2512.22402/) (2025)
- [Beyond Query Memorization: Large Language Model Routing with Query Decomposition and Historical Matching](https://mlsanity.com/paper/2605.25558/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) have demonstrated human-like instruction-following abilities, particularly those exceeding 100 billion parameters. The combined capability of some smaller, resource-friendly LLMs can address most of the instructions that larger LLMs excel at. In this work, we explore how to route the best-performing LLM for each instruction to achieve better overall performance. We develop a new paradigm, constructing capability instructions with model capability representation, user instruction, and performance inquiry prompts to assess the performance. To learn from capability instructions, we introduce a new end-to-end framework called Model Selection with Aptitude Test (Model-SAT), which generates positive and negative samples based on what different models perform well or struggle with. Model-SAT uses a model capability encoder that extends its model representation to a lightweight LLM. Our experiments show that Model-SAT understands the performance dimensions of candidate models and provides the probabilities of their capability to handle various instructions. Additionally, during deployment, a new model can quickly infer its aptitude test results across 50 tasks, each with 20 shots. Model-SAT performs state-of-the-art model routing without candidate inference and in real-world new model-released scenarios. The code is available at https://github.com/Now-Join-Us/CIT-LLM-Routing