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Route to Reason: Adaptive Routing for LLM and Reasoning Strategy Selection
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Route to Reason: Adaptive Routing for LLM and Reasoning Strategy Selection
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The inherent capabilities of a language model (LM) and the reasoning strategies it employs jointly determine its performance in reasoning tasks. While test-time scaling is regarded as an effective approach to tackling complex reasoning tasks, it incurs substantial computational costs and often leads to "overthinking", where models become trapped in "thought pitfalls". To address this challenge, we propose Route-To-Reason (RTR), a novel unified routing framework that dynamically allocates both LMs and reasoning strategies according to task difficulty under budget constraints. RTR learns compressed representations of both expert models and reasoning strategies, enabling their joint and adaptive selection at inference time. This method is low-cost, highly flexible, and can be seamlessly extended to arbitrary black-box or white-box models and strategies, achieving true plug-and-play functionality. Extensive experiments across seven open source models and four reasoning strategies demonstrate that RTR achieves an optimal trade-off between accuracy and computational efficiency among all baselines, achieving higher accuracy than the best single model while reducing token usage by over 60%.
Forward citations
Cited by 12 Pith papers
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DART: Draft-Agreement Routing for Training-Free Adaptive Thinking Budgets in Hybrid Reasoning Models
DART is a training-free router that accepts direct answers on draft agreement and allocates thinking budgets via draft entropy on disagreement, reporting accuracy gains and token reductions on math and code benchmarks...
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On Time, Within Budget: Constraint-Driven Online Resource Allocation for Agentic Workflows
MCPP is a Monte Carlo simulation-based online planner that improves the probability of agentic workflows completing successfully under explicit budget and deadline constraints compared to baselines on CodeFlow and Pro...
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SWE-Router: Routing in Multi-turn Agentic Software Engineering Tasks
SWE-Router introduces trajectory-conditioned value-based routing for LLM agents on SWE tasks, with a Bayes-optimality theorem and empirical cost savings while retaining most strong-model performance.
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DLLG: Dynamic Logit-Level Gating of LLM Experts
DLLG learns token-level fusion weights for LLM experts from sparse response supervision and outperforms routing, ensembling, and merging baselines on reasoning and code tasks.
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SeqRoute: Global Budget-Aware Sequential LLM Routing via Offline Reinforcement Learning
SeqRoute applies offline RL with CQL and Hindsight Budget Relabeling to sequential LLM routing under global budgets, claiming 6.0-73.5% cost reduction, maintained or improved quality, and under 1% bankruptcy rate.
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On Time, Within Budget: Constraint-Driven Online Resource Allocation for Agentic Workflows
MCPP uses Monte Carlo simulations of workflow executions to dynamically allocate resources and replan, raising constrained completion probability over baselines on CodeFlow and ProofFlow.
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Adaptive Test-Time Compute Allocation for Reasoning LLMs via Constrained Policy Optimization
A Lagrangian-relaxation plus imitation-learning pipeline adaptively allocates test-time compute to LLMs, outperforming uniform baselines by up to 12.8% relative accuracy on MATH while staying within a fixed average budget.
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R2-Router: A New Paradigm for LLM Routing with Reasoning
R2-Router jointly selects the LLM and an output-token budget, modeling each model as a quality-cost curve rather than a fixed point, and reports 4-5x cost savings on its new R2-Bench.
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Atlas: Orchestrating Heterogeneous Models and Tools for Multi-Domain Complex Reasoning
A dual-path router (clustering + RL) that jointly selects LLM and tool per query outperforms single-model routers and GPT-4o on 15 benchmarks.
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Spatial Reasoning via Modality Switching Between Language and Symbolic Representation
Introduces a trustworthiness-and-complexity switching metric that lets LLMs choose between language and grid modalities for spatial reasoning, yielding up to 42% gains in tested settings.
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Spatial Reasoning via Modality Switching Between Language and Symbolic Representation
Introduces a modality-switching mechanism for LLMs on spatial reasoning tasks using a trustworthiness and complexity based metric, showing up to 42% performance improvement.
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From Sampled Outcomes to Capability Distributions: Rethinking Supervision for LLM Routing
DARS replaces single-shot response labels with distribution-aware supervision derived from input and output uncertainty to produce more reliable LLM routing policies.
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