Collaborative LLM Inference via Planning for Efficient Reasoning

• URL: http://arxiv.org/abs/2506.11578v1
• Date: Fri, 13 Jun 2025 08:35:50 GMT
• Title: Collaborative LLM Inference via Planning for Efficient Reasoning
• Authors: Byeongchan Lee, Jonghoon Lee, Dongyoung Kim, Jaehyung Kim, Jinwoo Shin,
• Abstract summary: We propose a test-time collaboration framework in which a planner model first generates a plan, defined as a distilled and high-level abstraction of the problem.<n>Small and large models take turns acting as planner and reasoner, exchanging plans in a multi-round cascade to collaboratively solve complex tasks.<n>Our method achieves accuracy comparable to strong proprietary models alone, while significantly reducing reliance on paid inference.
• Score: 50.04696654679751
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) excel at complex reasoning tasks, but those with strong capabilities (e.g., whose numbers of parameters are larger than 100B) are often accessible only through paid APIs, making them too costly for applications of frequent use. In contrast, smaller open-sourced LLMs (e.g., whose numbers of parameters are less than 3B) are freely available and easy to deploy locally (e.g., under a single GPU having 8G VRAM), but lack suff icient reasoning ability. This trade-off raises a natural question: can small (free) and large (costly) models collaborate at test time to combine their strengths? We propose a test-time collaboration framework in which a planner model first generates a plan, defined as a distilled and high-level abstraction of the problem. This plan serves as a lightweight intermediate that guides a reasoner model, which generates a complete solution. Small and large models take turns acting as planner and reasoner, exchanging plans in a multi-round cascade to collaboratively solve complex tasks. Our method achieves accuracy comparable to strong proprietary models alone, while significantly reducing reliance on paid inference. These results highlight planning as an effective prior for orchestrating cost-aware, cross-model inference under real-world deployment constraints.

Related papers

• Route-and-Reason: Scaling Large Language Model Reasoning with Reinforced Model Router [9.580226379350737]
  Multi-step reasoning has proven essential for enhancing the problem-solving capabilities of Large Language Models.<n>Yet, many reasoning steps are relatively simple and can be handled by more efficient smaller-scale language models.<n>We propose R2-Reasoner, a novel framework that enables collaborative reasoning across heterogeneous LLMs.
  arXiv  Detail & Related papers  (2025-06-06T09:18:56Z)
• LightRouter: Towards Efficient LLM Collaboration with Minimal Overhead [19.573553157421774]
  Light is a novel framework designed to systematically select and integrate a small subset of LLMs from a larger pool.<n>Experiments demonstrate that Light matches or outperforms widely-used ensemble baselines, achieving up to a 25% improvement in accuracy.<n>This work introduces a practical approach for efficient LLM selection and provides valuable insights into optimal strategies for model combination.
  arXiv  Detail & Related papers  (2025-05-22T04:46:04Z)
• Plan and Budget: Effective and Efficient Test-Time Scaling on Large Language Model Reasoning [19.258292534503887]
  Plan-and-Budget is a model-agnostic, test-time framework that decomposes complex queries into sub-questions and allocates token budgets based on estimated complexity using adaptive scheduling.<n>Plan-and-Budget improves reasoning efficiency across a range of tasks and models, achieving up to +70% accuracy gains, tangential -39% token reduction, and +187.5% improvement in $E3$.
  arXiv  Detail & Related papers  (2025-05-22T01:56:29Z)
• CoLA: Collaborative Low-Rank Adaptation [3.421904493396495]
  Fine-tuning a pre-trained model for specific tasks achieves strong performance; however, it is computationally expensive and inefficient.<n>LoRA, in particular, has proven effective, but its application to multi-task scenarios is limited by interference between tasks.<n>We propose CoLA, a more flexible LoRA architecture and three collaborative strategies to enhance performance by better utilizing the quantitative relationships between $A$ and $B$.
  arXiv  Detail & Related papers  (2025-05-21T12:46:42Z)
• Scalable Chain of Thoughts via Elastic Reasoning [61.75753924952059]
  Elastic Reasoning is a novel framework for scalable chain of thoughts.<n>It separates reasoning into two phases--thinking and solution--with independently allocated budgets.<n>Our approach produces more concise and efficient reasoning even in unconstrained settings.
  arXiv  Detail & Related papers  (2025-05-08T15:01:06Z)
• Improving Large Models with Small models: Lower Costs and Better Performance [81.55672406002715]
  We propose Data Shunt$+$ (DS$+$), a general paradigm for collaboration of small and large models. For instance, ChatGPT achieves an accuracy of $94.43%$ on Amazon Product sentiment analysis, and DS$+$ achieves an accuracy of $95.64%$, while the cost has been reduced to only $31.18%$.
  arXiv  Detail & Related papers  (2024-06-15T14:44:43Z)
• MAP: Low-compute Model Merging with Amortized Pareto Fronts via Quadratic Approximation [80.47072100963017]
  We introduce a novel and low-compute algorithm, Model Merging with Amortized Pareto Front (MAP)<n>MAP efficiently identifies a set of scaling coefficients for merging multiple models, reflecting the trade-offs involved.<n>We also introduce Bayesian MAP for scenarios with a relatively low number of tasks and Nested MAP for situations with a high number of tasks, further reducing the computational cost of evaluation.
  arXiv  Detail & Related papers  (2024-06-11T17:55:25Z)
• Towards Efficient LLM Grounding for Embodied Multi-Agent Collaboration [70.09561665520043]
  We propose a novel framework for multi-agent collaboration that introduces Reinforced Advantage feedback (ReAd) for efficient self-refinement of plans. We provide theoretical analysis by extending advantage-weighted regression in reinforcement learning to multi-agent systems. Experiments on Over-AI and a difficult variant of RoCoBench show that ReAd surpasses baselines in success rate, and also significantly decreases the interaction steps of agents.
  arXiv  Detail & Related papers  (2024-05-23T08:33:19Z)
• Optimising Calls to Large Language Models with Uncertainty-Based Two-Tier Selection [80.63946798650653]
  Decision centers on whether to use a large LLM with better performance or a smaller one with reduced costs. We propose a simpler solution; we use only the uncertainty of the generations of the small LLM as the decision criterion. Our experiments reveal this simple solution optimally balances cost and performance, outperforming existing methods on 25 out of 27 experimental setups.
  arXiv  Detail & Related papers  (2024-05-03T14:38:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.