A Goal Without a Plan Is Just a Wish: Efficient and Effective Global Planner Training for Long-Horizon Agent Tasks

Baobao Chang; Fanchao Qi; Gang Chen; Haozhe Zhao; Kangyang Luo; Maosong Sun; Minjia Zhang; Shuzheng Si

arxiv: 2510.05608 · v3 · submitted 2025-10-07 · 💻 cs.CL

A Goal Without a Plan Is Just a Wish: Efficient and Effective Global Planner Training for Long-Horizon Agent Tasks

Shuzheng Si , Haozhe Zhao , Kangyang Luo , Gang Chen , Fanchao Qi , Minjia Zhang , Baobao Chang , Maosong Sun This is my paper

Pith reviewed 2026-05-18 09:11 UTC · model grok-4.3

classification 💻 cs.CL

keywords long-horizon tasksglobal plannerplan-and-execute frameworkLLM agentsfine-tuningrule-based reinforcement learningconsensus filteringexecutor capability gain

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The pith

A trained global planner lets LLM executors succeed on long-horizon tasks by replacing trial-and-error with guided steps, at eight times lower training cost and without manual plans or new data.

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

The paper shows how to build a separate planner that first learns from plans generated by a stronger language model and then gets sharpened through reinforcement learning so that an executor agent can complete multi-step tasks more reliably. The approach starts by creating many candidate plans for each task instruction, keeping only those that multiple similar generations agree on, and uses those filtered examples to fine-tune the planner. A second stage then rewards the planner whenever its output helps the executor improve its own performance on tasks of different difficulty levels. If this holds, agents stop wasting actions on repeated mistakes or invented steps because the planner supplies a coherent sequence upfront and the whole process runs without people writing examples or collecting fresh data.

Core claim

EAGLET trains a plug-and-play global planner in two stages: high-quality plans are first synthesized from an advanced LLM and filtered by a homologous consensus strategy to provide a cold-start fine-tuning signal, after which a rule-based reinforcement learning stage applies an executor capability gain reward to adapt the planner to instructions of varying difficulty.

What carries the argument

The two-stage EAGLET process that combines consensus-filtered plan synthesis for fine-tuning with a subsequent rule-based RL stage driven by executor capability gain reward.

If this is right

Executor agents achieve higher success rates on long-horizon tasks than previous methods.
Training the planner requires roughly one-eighth the compute of reinforcement-learning baselines.
The planner works for task instructions of different difficulty levels after the RL stage.
No manual plan writing or additional human-collected data is needed to reach the reported gains.

Where Pith is reading between the lines

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

The same synthesis-plus-filtering step could be reused to create planners for other executor architectures without redesigning data pipelines.
If the consensus filter removes most low-quality plans, similar filtering might help reduce hallucination when generating training data for other agent skills.
Testing the planner on tasks longer than those in the original experiments would show whether the capability-gain reward continues to scale.

Load-bearing premise

Plans generated by the advanced language model and retained through consensus filtering supply training signals that genuinely improve the executor's long-horizon behavior without injecting undetected errors or biases.

What would settle it

Running the three long-horizon agent tasks with executors paired to the EAGLET planner versus the same executors without the planner or with prior planners and checking whether success rates rise while training compute falls by the claimed factor.

read the original abstract

Agents based on large language models (LLMs) struggle with brainless trial-and-error and generating hallucinatory actions due to a lack of global planning in long-horizon tasks. In this paper, we introduce a plan-and-execute framework and propose EAGLET, an efficient and effective planner training method to enhance the executor agent's planning abilities without human effort. Specifically, we train a plug-and-play global planner through a two-step process: we first synthesize high-quality plans from an advanced LLM using our proposed homologous consensus filtering strategy, and apply fine-tuning as a cold start. Moreover, we further improve the planner with a rule-based reinforcement learning stage using a novel executor capability gain reward, ensuring it can handle task instructions of varying difficulty. Experiments on three long-horizon agent tasks show that executor agents equipped with our planner outperform existing methods, achieving new state-of-the-art performance. Meanwhile, EAGLET reduces training costs by 8x compared to RL-based baselines, and it does not require manual effort or extra training data, offering an efficient and effective solution.

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

EAGLET gives a workable two-stage recipe for training planners from LLM-synthesized plans and executor-gain RL, but the lack of direct plan-quality checks leaves the source of the gains unclear.

read the letter

The main thing to know is that EAGLET trains a planner for long-horizon LLM agents in two stages: first synthesizing plans from a strong LLM with homologous consensus filtering and fine-tuning them as a cold start, then applying rule-based RL using a reward that measures gains in executor performance. This setup delivers new state-of-the-art results on three tasks while cutting training costs by a factor of eight and skipping any need for human data. What the paper does well is lay out a practical pipeline that combines synthetic data generation with targeted reinforcement learning. The filtering strategy and the executor-gain reward are specific choices that address the cost and data issues in prior work on agent planning. The efficiency gain is a solid point in its favor, especially for groups that cannot afford full RL runs from scratch. The soft spots center on validation of the plans themselves. The method assumes the consensus-filtered outputs provide clean, effective training signal, but the results focus on final task performance rather than direct checks like plan executability rates or how much the plans diverge from what a human would write. This leaves room for the possibility that executor improvements drive most of the gains rather than the planner learning robust global structure. The abstract mentions SOTA and cost savings, but without details on run counts or statistical significance in the provided summary, those claims need the full experimental section to hold up. This work is for people developing autonomous agents that handle extended sequences of actions. Readers interested in efficient training methods for planners will find the recipe useful even if they adapt parts of it. The paper shows clear thinking on the problem and engages with the practical constraints, so it deserves a serious referee who can probe the plan quality and run more controls. I would send this to peer review.

Referee Report

2 major / 2 minor

Summary. The paper proposes EAGLET, a plan-and-execute framework for long-horizon LLM agents that trains a plug-and-play global planner in two stages: (1) synthesize plans from an advanced LLM using homologous consensus filtering, followed by fine-tuning as cold-start; (2) rule-based RL with a novel executor capability gain reward to handle varying task difficulty. Experiments on three long-horizon tasks claim new SOTA performance for equipped executors plus an 8x training cost reduction versus RL baselines, with no manual effort or extra data required.

Significance. If the central performance claims hold under proper controls, the work would provide a practical, low-cost route to global planning in LLM agents that avoids human annotation and reduces RL training expense by nearly an order of magnitude. The plug-and-play design and focus on executor capability gain as reward signal could influence subsequent agent architectures for long-horizon tasks.

major comments (2)

[Abstract and §4] Abstract and §4 (Experiments): the claims of SOTA results and 8x cost reduction are presented without reported number of runs, statistical tests, variance across seeds, or ablation isolating the planner from executor fine-tuning alone. This prevents evaluation of whether downstream gains are attributable to the learned global plans rather than other factors.
[§3.2] §3.2 (Homologous consensus filtering): the method's effectiveness rests on the assumption that filtered LLM-synthesized plans supply clean, unbiased training signal. No plan-level metrics (executability rate, factual consistency with task constraints, or divergence from human-authored plans) are reported, leaving open the possibility that observed improvements arise from executor fine-tuning rather than planner quality.

minor comments (2)

[§3.3] Define the precise formula for the executor capability gain reward and clarify how it is normalized across tasks of varying difficulty.
Add a table or figure showing plan quality statistics (e.g., executability, length distribution) before and after filtering to support the filtering strategy.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback and for recognizing the potential practical value of EAGLET. We address each major comment below in detail. Where the comments identify gaps in statistical reporting or supporting analyses, we have revised the manuscript accordingly.

read point-by-point responses

Referee: [Abstract and §4] Abstract and §4 (Experiments): the claims of SOTA results and 8x cost reduction are presented without reported number of runs, statistical tests, variance across seeds, or ablation isolating the planner from executor fine-tuning alone. This prevents evaluation of whether downstream gains are attributable to the learned global plans rather than other factors.

Authors: We agree that the original submission would benefit from explicit statistical reporting and an ablation isolating the planner. In the revised manuscript we now report all main results as means and standard deviations over five independent runs with different random seeds. We additionally include paired t-tests (with p-values) comparing EAGLET-equipped agents against the strongest baselines. To isolate the planner’s contribution, we added a controlled ablation in which the executor is fine-tuned on the same data without the global planner; the results show that the planner provides statistically significant further gains beyond executor fine-tuning alone. These updates appear in the revised §4 and a new appendix table. revision: yes
Referee: [§3.2] §3.2 (Homologous consensus filtering): the method's effectiveness rests on the assumption that filtered LLM-synthesized plans supply clean, unbiased training signal. No plan-level metrics (executability rate, factual consistency with task constraints, or divergence from human-authored plans) are reported, leaving open the possibility that observed improvements arise from executor fine-tuning rather than planner quality.

Authors: We acknowledge that plan-level diagnostics strengthen the causal link between filtering and downstream gains. In the revision we now report (i) executability rate of plans before and after homologous consensus filtering, (ii) factual consistency scores assigned by an independent LLM judge against task constraints, and (iii) a comparison of filtered plans against unfiltered LLM-generated plans (serving as a proxy for divergence, given that our method deliberately avoids human-authored plans). These metrics are presented in a new subsection of §3.2 and corroborate that filtering improves plan quality, which in turn drives the observed executor improvements. revision: yes

Circularity Check

0 steps flagged

No significant circularity in planner training chain

full rationale

The paper's method synthesizes plans from an external advanced LLM via homologous consensus filtering, applies fine-tuning as cold-start, then uses rule-based RL with an executor capability gain reward defined from measured downstream improvements. This chain relies on independent LLM outputs and task-level performance metrics rather than any self-definitional loop, fitted parameter renamed as prediction, or load-bearing self-citation. The derivation remains self-contained against external benchmarks with no reductions to inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim rests on the unverified assumption that LLM-generated plans after consensus filtering constitute high-quality supervision and that the rule-based reward accurately reflects planner quality across difficulty levels.

axioms (2)

domain assumption Advanced LLMs can generate plans of sufficient quality for long-horizon tasks when filtered by homologous consensus
Invoked in the first synthesis and filtering stage of the training pipeline.
domain assumption Executor capability gain provides a reliable and generalizable reward signal for improving the planner
Used to drive the rule-based RL stage and to handle varying task difficulty.

pith-pipeline@v0.9.0 · 5746 in / 1515 out tokens · 67261 ms · 2026-05-18T09:11:59.218274+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

?

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

we first synthesize high-quality plans from an advanced LLM using our proposed homologous consensus filtering strategy, and apply fine-tuning as a cold start... rule-based reinforcement learning stage using a novel executor capability gain reward
IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear

?

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

Experiments on three long-horizon agent tasks show that executor agents equipped with our planner outperform existing methods

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extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
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