Lookahead Routing for Large Language Models

Thanks for the valuable comments. We really appreciate your efforts to help us improve our paper. We carefully addressed your concerns below and sincerely hope that our reply resolves your concerns. Please let us know if you have any follow-up questions.

Q1: Concern about novelty.

A1: We would like to highlight that the core novelty of our work lies in addressing a critical limitation of existing routing methods: their inability to leverage potential model outputs during routing decisions. Current approaches rely solely on input features, neglecting the contextual nuances and implicit intent that emerge during response generation. Our proposed response-aware router introduces a paradigm shift by:

1. Predicting latent representations of candidate model outputs (via MLM/CLM-based exploration) to "foresee" their semantic contributions.

2. Enabling informed routing without full inference, which is particularly effective for complex/ambiguous queries requiring deeper semantic understanding.

This framework provides a new perspective for routing research, as evidenced by the significant performance gains across diverse tasks.

Q2: Regarding the necessity of the routing task.

A2: While LLMs continue to evolve, we emphasize that no single model consistently excels across all scenarios—different models often have complementary strengths (e.g., domain-specific expertise vs. general-purpose capabilities). Routing remains crucial to harness these strengths effectively, as supported by recent studies (e.g., [1][2]).

[1] Harnessing the Power of Multiple Minds: Lessons Learned from LLM Routing. ArXiv 2024.

[2] Routing to the Expert: Efficient Reward-guided Ensemble of Large Language Models. NAACL 2024.

Q3: Regarding inference cost in modeling.

A3: Thank you for raising this important point. While we agree that cost-aware routing is critical for practical deployments, we need to clarify that model routing research can generally be categorized into two main directions: (1) improving the prediction of candidate model performance (e.g., [1][2][3]), and (2) balancing multiple objectives such as performance, cost, and latency (e.g., [4][5][6]). Our work falls into the first category, focusing on incorporating candidate model responses to enhance performance prediction rather than explicitly addressing trade-offs between performance and cost.

That said, we believe our approach could complement multi-objective routing methods to improve overall effectiveness. Accurate performance prediction is foundational to any cost-performance trade-off; thus, our response-aware framework could be integrated with approaches like MixLLM [6] to extend routing objectives beyond pure performance—incorporating factors such as response length, inference latency, and computational cost. These objectives could be balanced using adjustable weighting factors tailored to specific requirements or resource constraints.

We appreciate your valuable suggestion and will include a discussion on this potential extension in the revised manuscript.

[1] Routing to the Expert: Efficient Reward-guided Ensemble of Large Language Models. NAACL 2024.

[2] RouterDC: Query-based Router by Dual Contrastive Learning for Assembling Large Language Models. NeurIPS 2024.

[3] SMOOTHIE: Label Free Language Model Routing. NeurIPS 2024.

[4] Fly-Swat or Cannon? Cost-Effective Language Model Choice via Meta-Modeling. WSDM 2024.

[5] RouteLLM: Learning to Route LLMs from Preference Data. ICLR 2025.

[6] MixLLM: Dynamic Routing in Mixed Large Language Models. NAACL 2025.

Q4: Regarding generalization over reasoning models.

A3: We acknowledge that evaluating reasoning models could further illustrate the generalization capability of our method. However, we emphasize that our approach has already been thoroughly tested across seven diverse benchmarks, including instruction following, math, and code. Our method consistently outperforms baselines across all these tasks, demonstrating robust generalization capabilities.

While the inference time for reasoning models on our hardware (RTX 3090) is prohibitively long, making it difficult to complete additional experiments within the rebuttal timeline, we are actively running these experiments and will share results during the discussion period.

We sincerely thank you for the detailed and positive comments. We take all comments seriously and do our best to address every concern raised. Please let us know if you have any follow-up questions.

Q1: Regarding a more granular analysis of Lookahead's performance.

A1: Thank you for this thoughtful suggestion. We agree that a detailed analysis of Lookahead's routing patterns would provide deeper insights into its effectiveness. To address this, we conducted an evaluation on math and code benchmarks (GSM8K, MATH, HumanEval, MBPP), comparing Lookahead with a query-only baseline where response-aware modeling is removed.

Our findings highlight that Lookahead demonstrates strong specialization awareness in its routing decisions:

• For math queries (GSM8K and MATH), Lookahead frequently routes to InternLM-2.5-20B-Chat and Qwen2.5-Coder-7B-Instruct—the top two models for math tasks as identified in Table 1—significantly more often than the baseline.

• For code queries (HumanEval and MBPP), it consistently selects Qwen2.5-Coder-7B-Instruct—the coding-specialized model—with higher accuracy compared to the baseline.

These results confirm that response-aware modeling enables Lookahead to better identify model specialties and make precise routing decisions tailored to task requirements. We will incorporate these analyses into the revised manuscript to further demonstrate our method’s strengths.

Q2: Regarding qualitative error analysis.

A2: To analyze Lookahead's errors qualitatively, we categorized test samples based on the number of candidate LLMs capable of providing correct responses (ranging from 0 to 5). We then evaluated Lookahead’s win rate against baselines on samples with 1–4 correct responses, where:

• Win: Lookahead routed to a correct response while baselines failed.

• Loss: Baselines succeeded while Lookahead did not.

• Tie: Both succeeded or failed simultaneously.

Our findings show that Lookahead performs better on more complex queries, particularly those where only a few models can generate correct responses. This highlights the strength of response-aware modeling in understanding and routing for challenging tasks.

To further investigate areas for improvement, we observed that MLM-based Lookahead is less effective on mathematical problems compared to instruction-following tasks. This limitation arises because our current implementation models differences only in the first $m$ tokens of candidate responses. For example, in GSM8K math problems, most models restate the problem before solving it—resulting in highly similar initial tokens across different models and overly similar latent representations that can mislead routing decisions.

A promising optimization would be to adaptively model token positions with the greatest semantic differences among candidate responses rather than focusing fixedly on initial tokens. We will include these results and analyses in the revised manuscript along with potential future directions for improvement.

Q3: Regarding the computational complexity with more candidate models.

A3: Thank you for raising this important point. We acknowledge that response-aware modeling in Lookahead introduces additional complexity compared to a query-only baseline. However, this overhead is minimal relative to LLM inference and is justified by the significant performance gains it delivers. To clarify, response-aware modeling predicts latent representations of candidate responses and uses these predictions to guide model selection without requiring full inference. Below, we detail the computational efficiency of our approach:

• CLM-based implementation:

  • During training, the model generates responses using input sequences ($x\|MID_t$), where $MID_t$ represents a model identifier. At inference time, only the hidden state at $MID_t$ is used to predict response quality—avoiding actual generation.

  • To further optimize efficiency, all model IDs can be packed into a single sequence ($x\|MID_1\|\dots\|MID_T$) with modified attention masks ensuring proper isolation between identifiers. This requires just one forward pass with an additional $T$ tokens compared to a simple classifier.

• MLM-based implementation:

  • All candidate responses are reconstructed jointly in one forward pass instead of multiple inferences.

  • While introducing $m*T$ extra tokens (where $m$ is fixed per response), experiments show that small values of $m$ (16–64 tokens) yield substantial performance improvements (as shown in Figure 7(b)).

Experimental results demonstrate:

• With five candidate models:

  • The CLM-based Lookahead adds only ~4.6% computational cost compared to a query-only router while maintaining high routing accuracy.

  • The MLM-based variant incurs slightly higher costs but remains under 5% of the computation required by Qwen2.5-Coder-7B-Instruct—the smallest LLM in our pool—to generate just its first token for queries averaging 88 tokens.

• As the number of candidates increases from three to eight:

  • Both variants exhibit slow growth in latency due to efficient batching mechanisms.

  • While MLM-based Lookahead scales faster because it introduces $m*T$ extra tokens per model, its overall cost remains negligible compared to LLM decoding overheads.

These findings confirm that Lookahead's design balances improved routing decisions with minimal added complexity even as candidate pools grow larger. We will incorporate these analyses and experimental results into the revised paper for clarity and completeness.

Q4: Regarding retrain Lookahead when the candidate model pool is updated.

A4: We acknowledge that significant updates to candidate models or the addition of new LLMs would require retraining Lookahead. However, we want to clarify that this is not a limitation unique to our approach but rather a fundamental characteristic of most routing frameworks. Routing methods inherently rely on learning performance characteristics from training queries, and any substantial changes in model capabilities or the introduction of new models necessitate updating the router to adapt to these shifts.

That said, routers are typically lightweight and designed for rapid retraining. For example, our MLM-based Lookahead implementation (using ModernBERT-base) can be fully retrained in under one hour on a single NVIDIA RTX 3090 GPU. This low computational cost ensures that Lookahead remains practical even in dynamic environments where periodic model updates occur.

Q5: Regarding the risk of amplifying biases and misinformation.

A5: Thank you for highlighting this limitation. We acknowledge the inherent risk that routing decisions could amplify biases or misinformation if reward models fail to detect biased or erroneous outputs from LLMs, potentially propagating issues present in the training data.

To mitigate this, we propose deploying ensemble methods with diverse reward models, which can enhance detection accuracy and reduce the likelihood of misclassifying biased or incorrect responses as high-quality. Furthermore, incorporating fairness-aware metrics and robust evaluation criteria into our framework could provide additional safeguards against these risks.

We will include a detailed discussion on these potential risks and mitigation strategies in the revised manuscript.

Thank you for your thoughtful review and valuable feedback. Below, we address your concerns in detail.

Q1: Regarding additional complexity and overhead introduced by Lookahead.

A1: While we acknowledge that Lookahead introduces some additional complexity compared to simple classifiers, we would like to clarify that this overhead is minimal and justified by the significant performance gains it achieves. Our design specifically avoids generating proxy responses or requiring multiple forward passes for each query:

• CLM-Based Implementation: At inference time, the hidden state at $MID_t$ encodes sufficient information to predict response quality—avoiding actual generation (as described in L189–L190). Furthermore, all model IDs can be packed into a single sequence ($x\|MID_1\|\dots\|MID_T$) with modified attention masks. This requires just one forward pass with an additional $T$ tokens compared to a simple classifier.
• MLM-Based Implementation: All candidate responses are reconstructed jointly in one forward pass instead of performing multiple inferences, which introduces $m \times T$ extra tokens.

To quantify the overhead:

• The CLM-based Lookahead adds only ~4.6% more computational cost than a simple classifier.
• The MLM-based variant incurs slightly higher costs but remains under 5% of the computational cost required by our smallest candidate model (Qwen2.5-Coder-7B-Instruct) to generate just its first token for queries averaging 88 tokens.

These results demonstrate that both implementations maintain high efficiency while delivering superior routing accuracy. We will include these analyses and clarifications in the revised manuscript to address your concerns comprehensively.

Q2: Regarding the scalability of Lookahead.

A2: We would like to clarify the scalability of Lookahead from three key perspectives:

1. Computational Efficiency: While we acknowledge that computational cost grows linearly with the number of candidate models, as explained in A1, Lookahead only introduces overhead for $T$ or $m\times T$ additional tokens.
2. Resource Limitation: Our MLM-based Lookahead leverages a ModernBERT-base backbone capable of handling input sequences up to 8K tokens (typically capable of 100+ candidate models), requiring just 8.4GB of video memory.
3. Empirical Validation: Due to time constraints during rebuttal preparation, conducting experiments on dozens of models was infeasible given data collection requirements. However, we expanded our existing pool to eight candidate models—and compared performance and efficiency between Lookahead and multi-label classifiers (MLC).

Results show:

• Performance improves when expanding from three to five candidates due to stronger model additions.
• Performance gradually declines when expanding further (to eight candidates), as weaker models increase routing error risks.
• Notably, Lookahead consistently outperforms classifier baselines, while maintaining computational costs and latency aligned with theoretical expectations.

We will include these analyses in the revised manuscript along with detailed experimental results supporting its scalability claims comprehensively!

Q3: Regarding the cost of collecting training data.

A3: We would like to clarify that this challenge is not unique to our approach but rather inherent to most routing methodologies. While previous routing methods did not incorporate response content directly into router training, they still required generating these same responses to obtain scores used as training labels. Furthermore, Lookahead significantly mitigates this burden through remarkable data efficiency (refer to Section 5.4 for details).

Q4: Regarding the application of other routing objectives.

A4: Thank you for this insightful comment, which highlights an important direction for validating the flexibility of our framework. Due to the limited time, we focused on integrating one representative objective (the KL divergence objective) to demonstrate the extensibility of our method. As shown below, the CLM-based Lookahead achieves a 6.9% higher normalized score compared to standard ZOOTER, while the MLM-based variant demonstrates an even more substantial improvement of 13.9%.

We will include these experimental results and discussions in the revised manuscript.

Q5: Regarding the training data construction and evaluation metrics.

A5: We would like to clarify the training data construction process and evaluation metrics as follows:

• Training Set Construction: During the creation of open-ended training data, we utilize the reward model to evaluate candidate responses. These scores are then normalized to the [0,1] interval for consistency.
• Metrics for Instruction-Following Benchmarks: In benchmarks such as MT-Bench and AlpacaEval, evaluations are based on metrics like average score or win rate of model outputs across prompts.

Due to space limitations, more details can be found in Section 5.1 and Appendix C.1.

Q6: Regarding further efficiency optimization, such as early stopping and initial screening.

A6: Thank you for your valuable suggestions. We agree that exploring such optimizations could further improve efficiency. While adopting a widely used experimental setting without optimization in this paper, we will actively investigate additional optimization strategies that could work in conjunction with Lookahead, including hierarchical routing approaches and adaptive model selection techniques that could reduce inference overhead.

Q7: Regarding the trade-off between performance and computational cost.

A7: To clarify, model routing research can generally be categorized into two main directions:

1. Improving the prediction of candidate model performance (e.g., [1][2][3]).
2. Balancing multiple objectives, such as performance, cost, and latency (e.g., [4][5][6]).

Our work falls into the first category, focusing on incorporating candidate model responses to enhance performance prediction rather than explicitly addressing trade-offs between performance and cost.

References:
[1] Routing to the Expert. NAACL 2024.
[2] RouterDC. NeurIPS 2024.
[3] Smoothie. NeurIPS 2024.
[4] Fly-Swat or Cannon? WSDM 2024.
[5] RouteLLM. ICLR 2025.
[6] MixLLM. NAACL 2025.

Q8: Regarding cases where Lookahead might fail or underperform

A8: We acknowledge that analyzing failure cases can provide valuable insights to guide potential improvements. Specifically, we found that the MLM-based Lookahead shows less advantage on mathematical problems compared to instruction-following tasks. This is likely because our current implementation models differences only in the first $m$ tokens of responses. For example, as shown below, models often restate the problem before solving it, resulting in highly similar prefixes across different models, leading to overly similar latent representations that can mislead routing decisions. A promising optimization would be adaptively identifying the most informative spans among responses, rather than uniformly focusing on prefixes.

1. To determine how many years it will take ...
2. To find the number of years it will take ...

We will include these analyses in the revised manuscript.

Thank you for your detailed review and insightful questions on our paper. Below, we address your concerns in detail. Please let us know if you have any follow-up questions.

Q1: Regarding the reason for reimplementing baselines.

A1: To clarify, the primary reason for reimplementing baseline methods is the absence of publicly available training datasets that include model responses, which are essential for our response-aware routing framework. Since we needed to implement all methods on our customized training set, we chose to use stronger backbones and keep them consistent across all approaches to ensure a fair comparison.

Q2: Regarding the strengths of Lookahead over pretrained embedding based methods.

A2: To clarify, while certain pretrained embedding-based methods demonstrate competitive results on specific benchmarks, such as kNN on MATH and SMOOTHIE on MBPP. We would like to emphasize that response-aware modeling enables Lookahead to achieve SOTA performance not only in overall metrics but also in 5 out of these 7 individual benchmarks. Below are three key strengths of Lookahead compared to pretrained embedding-based approaches:

1. Predictive Latent Representations: By forecasting latent representations of potential model outputs, Lookahead captures semantic nuances, which allows for more contextually informed routing decisions compared to embedding methods that rely solely on query similarity.

2. Inference Efficiency: Unlike similarity-based methods (e.g., kNN or SMOOTHIE), which compute distances between queries and training samples or cluster centers during inference—a computationally expensive step—Lookahead generates its response-aware features in a single forward pass, significantly reducing latency while maintaining high accuracy.

Q3: Regarding the accuracy-efficiency tradeoff.

A3: To clarify, the statement in our abstract about "improving the efficiency of multi-model systems" refers to the fundamental advantage of routing approaches compared to alternative multi-model strategies such as ensemble methods or cascaded inference. As established in prior routing research [1][2], routing queries to the suitable model inherently reduces computational overhead of inference on all candidate models introduced by ensemble methods while maintaining performance advantages over using any single model alone. Our work builds upon this established efficiency foundation and focuses on improving routing accuracy.

To further demonstrate that Lookahead achieves an accuracy-efficiency tradeoff on par with other routing methods and surpasses single model inference or model ensembling, we compared the MLM-based Lookahead with its multi-label classifier baseline, the best candidate model, and an ensembling method where a reward model selects the best candidate responses. As shown below, both model ensembling and routing surpass the best single model by a large margin in performance. However, model ensembling incurs a heavy computational cost by generating with totally 83.8B parameters for each query, while MLC and Lookahead both reduce this cost to only about 21%.

We will include these results to the updated paper.

[1] Harnessing the Power of Multiple Minds: Lessons Learned from LLM Routing. ArXiv 2024.

[2] Routing to the Expert: Efficient Reward-guided Ensemble of Large Language Models. NAACL 2024.

Q4: Regarding cases where Lookahead might fail or underperform.

A4: We acknowledge that the analysis of routing errors can indeed reveal valuable insights that guide potential improvements. For example, we observed that the MLM-based Lookahead shows less advantage on mathematical problems compared to instruction-following tasks. This appears to be because our current implementation models differences only in the first $m$ tokens of responses. As an example from GSM8K shown below, in mathematical problems, models tend to restate the problem before solving, resulting in similar beginnings across different models. This similarity in initial tokens leads to overly similar latent representations that can mislead the router. This example highlights a key opportunity for improvement. A promising optimization is to adaptively identify the most informative spans among responses, rather than uniformly focusing on the prefix.

We will include these results and analysis in the revised paper.

Q5: Regarding the reason for using BCE loss.

A5: Thank you for raising this important question. While the final routing decision selects a single model, we formulate the training process as a multi-label classification problem rather than multi-class classification following previous works [1][2][3]. Reasons for this distinction are outlined below:

1. In LLM routing, multiple models may produce high-quality responses for the same query. Using BCE loss allows us to independently evaluate each model's capability on a given query, rather than forcing an artificial exclusivity among candidate models.

2. This multi-label approach provides richer training signals compared to standard multi-class cross-entropy, as it preserves information about which models perform well rather than only identifying the single "best" model for each training example.

3. During inference, we select only one LLM with the highest predicted likelihood to generate the final response for the best performance.

We will incorporate these clarifications into the revised paper.

[1] Large Language Model Routing with Benchmark Datasets. ArXiv 2023.

[2] Harnessing the Power of Multiple Minds: Lessons Learned from LLM Routing. ArXiv 2024.

[3] EmbedLLM: Learning Compact Representations of Large Language Models. ICLR 2025.

Q6: Regarding the setting and cost of $m$.

A6: Thank you for your insightful question regarding the selection of parameter m in our MLM-based implementation. We appreciate the opportunity to clarify this important design choice.

Our experiments in Appendix E.2 demonstrate that $m$ does not need to cover all possible response lengths. Specifically, we found that setting $m=64$ achieves the best performance in our experimental setup, despite the fact that some responses may be longer and would consequently be truncated. This finding is supported by two key observations:

• Even for tasks requiring longer responses, the initial segments often contain sufficient semantic information to distinguish between the capabilities of different candidate models.

• As shown in Figure 7(b), increasing $m$ beyond 64 actually leads to performance degradation due to error accumulation during long-range prediction by the lightweight router.

To further analyze the cost introduced by repeated model ID tokens, we compare the computational cost of Lookahead to its baseline and the smallest candidate LLM. As shown below, we acknowledge that the MLM-based Lookahead introduces some extra computational cost. However, when compared to the computation that LLMs cost to generate the response and considering the marginal performance improvement of Lookahead, the extra computational cost is acceptable.

We will incorporate these analyses and experimental results in the revised paper.

Q7: Regarding the risk of bias introduced to the performance metrics by the reward model.

A7: Thank you for your thoughtful comments. We address them from the following perspectives:

• To clarify, UltraFeedback serves as a part of the training set instead of a test set in our experiments. Therefore, any potential bias from the reward model does not affect our performance metrics on the evaluation benchmarks.

• For open-ended instruction-following tasks, we utilize widely used benchmarks (AlpacaEval-2, Arena-Hard, and MT-Bench), which utilize powerful LLMs like GPT-4-Preview-1106 as judges. These evaluation approaches have been demonstrated to align well with human preferences [1]. Additional details about our evaluation metrics are provided in Appendix B.

• We appreciate you bringing our attention to the potential confusion in Table 1. We acknowledge that there was a typographical error in L240. The "Reward Model Select" method reported in the table actually uses the same reward model (Skywork-Reward-Gemma-2-27B-v0.2) that was employed for annotating our training data. This result serves as an upper bound for routing methods in open-ended tasks, providing a reference point for the maximum achievable performance under our experimental setup.

[1] Judging LLM-as-a-judge with MT-Bench and Chatbot Arena. NeurIPS 2023.

Q8: Regarding the paragraph heading in L211.

A8: Thank you for your valuable comment regarding the use of "theoretical justification" in line 211. We agree that the term "theoretical justification" implies a level of formal theoretical analysis that the paragraph does not provide. Since it primarily presents our design rationale and conceptual motivation, we will revise the paragraph heading to "Design Rationale" to more accurately reflect the nature of our discussion. The content of the section will be adjusted accordingly to maintain consistency with this more precise characterization.