Efficient LLM Collaboration via Planning

Byeongchan Lee; Dongjun Lee; Dongyoung Kim; Jaehyung Kim; Jinwoo Shin; Jonghoon Lee; Kyungjoon Park

arxiv: 2506.11578 · v4 · submitted 2025-06-13 · 💻 cs.AI

Efficient LLM Collaboration via Planning

Byeongchan Lee , Jonghoon Lee , Dongyoung Kim , Jaehyung Kim , Kyungjoon Park , Dongjun Lee , Jinwoo Shin This is my paper

Pith reviewed 2026-05-19 09:46 UTC · model grok-4.3

classification 💻 cs.AI

keywords LLM collaborationplanningcost-efficient inferencemodel cascadingtest-time collaborationsmall and large modelsreasoning tasks

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The pith

Small and large language models collaborate through multi-stage planning to match large-model performance at far lower cost.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces COPE, a framework in which one model generates a plan that guides the next model in a cascade of planner-executor steps. Small and large models alternate these roles so that cheap local models handle much of the work while still solving hard tasks. Experiments across math reasoning, code generation, open-ended questions, and agent benchmarks show the combined system reaches accuracy levels comparable to large proprietary models. The central payoff is a sharp drop in the number of calls made to paid large-model APIs. This approach treats the generated plan as a compact, reusable prior that lets the two model sizes complement each other without needing full large-model reasoning at every step.

Core claim

COPE is a test-time collaboration method in which a planner model first produces a plan that serves as a lightweight intermediate representation; an executor model then follows that plan. Small and large models exchange these roles across multiple cascade stages, allowing the system to solve mathematical, coding, and agent tasks at performance levels comparable to large proprietary models while using far fewer large-model API calls.

What carries the argument

COPE, the multi-stage cascade in which plans generated by one model size guide execution by the other model size, alternating planner and executor roles.

If this is right

The method reaches accuracy comparable to large proprietary models on math, code, open-ended, and agent tasks.
Inference API cost drops substantially because fewer large-model calls are required.
Planning functions as an effective lightweight prior that transfers useful structure between model sizes.
The cascade can be applied at test time without retraining either model.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same planning hand-off might let teams combine open-source small models with occasional large-model calls in production systems.
If plans remain stable across domains, the approach could extend to multi-turn agent workflows where intermediate plans reduce repeated large-model queries.
Testing longer cascades or adding a verification step between stages would show whether error accumulation limits the method.

Load-bearing premise

Plans created by a smaller or cheaper model stay accurate enough to steer a larger model across several cascade stages without errors accumulating.

What would settle it

Run the same benchmarks with the planner and executor roles reversed and measure whether accuracy falls sharply or API cost savings disappear.

read the original abstract

Recently, large language models (LLMs) have demonstrated strong performance, ranging from simple to complex tasks. However, while large models achieve remarkable results across diverse tasks, they often incur substantial monetary inference cost, making frequent use impractical for many applications. In contrast, small models are often freely available and easy to deploy locally, but their performance on complex tasks remains limited. This trade-off raises a natural question: how can small and large models efficiently collaborate to combine their complementary strengths? To bridge this trade-off, we propose COPE, a test-time collaboration framework. A planner model first generates a plan that serves as a lightweight intermediate that guides a downstream executor model. Small and large models take turns acting as planner and executor, exchanging plans in a multi-stage cascade to collaboratively solve tasks. Through comprehensive experiments on benchmarks spanning mathematical reasoning, code generation, open-ended tasks, and agent tasks, we demonstrate that COPE achieves performance comparable to large proprietary models, while drastically reducing the inference API cost. These results highlight planning as an effective prior for cost-efficient inference.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

COPE's multi-stage planner-executor cascade between small and large models looks like a practical cost saver, but the risk of error propagation needs closer scrutiny.

read the letter

The one thing to know is that COPE lets small and large LLMs collaborate by taking turns generating plans and executing them in a cascade, which the authors say gets performance close to large models while cutting costs a lot. The paper introduces a clear test-time framework for this kind of collaboration. Instead of always using the large model or distilling knowledge, they have the models exchange plans across stages, with roles flipping. Experiments on mathematical reasoning, code generation, open-ended tasks, and agent tasks are used to back this up. They report that the approach matches large proprietary models in results but reduces the inference API cost significantly. This is the kind of empirical work that can be directly useful for testing hybrid setups. A soft spot is the handling of potential error propagation. The concern is that plans from smaller models could miss key steps, and then the executor follows a flawed path that later stages reinforce rather than correct. The paper would benefit from more detail on how they ensure plans stay informative across turns, perhaps through ablations on plan accuracy or recovery rates. If those checks are there, it strengthens the case; if not, the cost savings might come with occasional drops in reliability that aren't fully captured in the main metrics. Readers working on cost-efficient LLM applications or multi-model agent systems would find this relevant. It provides a concrete method to try out on their own tasks. The range of benchmarks helps show the scope. Overall, this deserves a serious referee. The collaboration pattern is worth examining in detail, and feedback on the experimental controls would help refine it.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes COPE, a test-time collaboration framework for LLMs in which planner and executor models of different sizes alternate roles in a multi-stage cascade. Plans generated by one model serve as lightweight scaffolds to guide execution by the other, with the goal of combining the strengths of small and large models. The central claim, supported by experiments on mathematical reasoning, code generation, open-ended tasks, and agent tasks, is that COPE achieves performance comparable to large proprietary models while drastically reducing inference API cost.

Significance. If the empirical results hold under rigorous controls, the work could meaningfully advance cost-efficient LLM deployment by demonstrating that planning can act as an effective prior for hybrid small-large model collaboration. The approach is practically relevant given the cost barriers of large models and the accessibility of small ones, and the breadth of evaluated task categories strengthens its potential applicability if the collaboration mechanism proves robust.

major comments (2)

Abstract and experimental results section: the claim that 'experiments demonstrate comparable performance and cost reduction' supplies no information on baselines, controls, statistical tests, or exact metrics. Without these, it is not possible to judge whether the data support the central claim that performance remains comparable while costs drop substantially.
Section 3 (cascade collaboration description): the assumption that plans produced by one model size remain sufficiently informative and low-error when used to guide execution by the other model size across multiple cascade stages is load-bearing for the cost-reduction argument. No quantitative evidence is provided that error rates stay sub-additive or that the large executor can recover from omissions in small-model plans without reverting to full large-model reasoning at every step; if plans lock the executor onto flawed trajectories, subsequent stages would compound rather than mitigate mistakes.

minor comments (2)

Abstract: consider briefly specifying the model sizes (e.g., 7B planner with 70B executor) used in the reported cascades to give immediate context for the claimed cost savings.
Notation throughout: ensure consistent terminology for 'planner' versus 'executor' and for the number of cascade turns to prevent ambiguity when describing role alternation.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments. We address each major point below and have revised the manuscript to strengthen the presentation of experimental details and the cascade mechanism.

read point-by-point responses

Referee: Abstract and experimental results section: the claim that 'experiments demonstrate comparable performance and cost reduction' supplies no information on baselines, controls, statistical tests, or exact metrics. Without these, it is not possible to judge whether the data support the central claim that performance remains comparable while costs drop substantially.

Authors: We agree that the abstract is high-level and omits specifics on baselines, metrics, and controls. The experimental results section does compare against direct large-model inference, small-model baselines, and prior collaboration methods using accuracy and API cost metrics, with some controls for model sizes. To improve clarity, we have revised the abstract to briefly reference the primary baselines (e.g., GPT-4 direct, small-model only) and key metrics, and added a pointer to the statistical significance tests now detailed in the appendix. revision: yes
Referee: Section 3 (cascade collaboration description): the assumption that plans produced by one model size remain sufficiently informative and low-error when used to guide execution by the other model size across multiple cascade stages is load-bearing for the cost-reduction argument. No quantitative evidence is provided that error rates stay sub-additive or that the large executor can recover from omissions in small-model plans without reverting to full large-model reasoning at every step; if plans lock the executor onto flawed trajectories, subsequent stages would compound rather than mitigate mistakes.

Authors: This concern about error propagation in multi-stage cascades is well-taken and central to the claims. While end-to-end results across math, code, and agent tasks indicate that performance does not degrade substantially (implying recovery occurs), we did not include explicit per-stage plan error analysis or recovery quantification. We have added a new analysis subsection with quantitative metrics on plan informativeness, error rates by model size, and executor correction rates, using held-out plan quality annotations. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical framework with external benchmarks

full rationale

The paper proposes COPE as a procedural test-time collaboration method in which planner and executor roles alternate between small and large models across cascade stages. The central performance claim is established solely through direct experimental comparisons on mathematical reasoning, code generation, open-ended, and agent benchmarks against large proprietary models and baselines. No equations, fitted parameters, or derivations appear in the manuscript; the method is described as a sequence of model calls rather than a mathematical reduction. No self-citations are used to justify uniqueness theorems, ansatzes, or load-bearing premises. The argument therefore rests on falsifiable empirical results measured against external benchmarks and does not reduce to its own inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The framework depends on the untested premise that planning produces a sufficiently faithful and compact signal for cross-model guidance; no free parameters, invented entities, or additional axioms are mentioned in the abstract.

axioms (1)

domain assumption Plans generated by one LLM can serve as effective lightweight intermediates that guide execution by another LLM without substantial information loss or error accumulation.
This premise is required for the cascade to preserve performance while reducing large-model usage.

pith-pipeline@v0.9.0 · 5723 in / 1293 out tokens · 32421 ms · 2026-05-19T09:46:42.711879+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

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unclear
Relation between the paper passage and the cited Recognition theorem.

We propose COPE, a test-time collaboration framework. A planner model first generates a plan that serves as a lightweight intermediate that guides a downstream executor model. Small and large models take turns acting as planner and executor, exchanging plans in a multi-stage cascade
IndisputableMonolith/Foundation/ArithmeticFromLogic.lean LogicNat recovery and embed_strictMono unclear

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unclear
Relation between the paper passage and the cited Recognition theorem.

Each inference round follows three steps: (i) sample a plan g, (ii) generate a solution y conditioned on g, and (iii) aggregate the extracted answers by majority vote.

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.