arxiv: 2605.06957 · v1 · submitted 2026-05-07 · 💻 cs.AI

Recognition: no theorem link

Learning and Reusing Policy Decompositions for Hierarchical Generalized Planning with LLM Agents

Shirin Sohrabi , Haritha Ananthakrishnan , Harsha Kokel , Kavitha Srinivas , Michael Katz

Authors on Pith no claims yet

Pith reviewed 2026-05-11 01:07 UTC · model grok-4.3

classification 💻 cs.AI

keywords hierarchical planningLLM agentspolicy decompositiongeneralized planningcomponent reusesemantic retrievaltask decomposition

0 comments

The pith

LLM agents generate effective plans for unseen tasks by reusing learned policy components from past successes.

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

The paper develops HCL-GP to combine generalized planning with hierarchical decomposition in LLM agents. It learns policies by breaking down successful executions into components, generalizing those components, and storing them for later reuse through semantic search. This dynamic reuse addresses key challenges in component learning, generalization, and retrieval. A sympathetic reader would care because it shows how classical planning ideas can enhance LLM performance without constant retraining, leading to better results on complex benchmarks. The evaluations demonstrate substantial gains, especially in challenging scenarios with new applications.

Core claim

The authors establish that a dynamic policy-learning method, which automatically decomposes successful executions into reusable parameterized components and organizes them in a library for compositional generation, allows LLM agents to create generalized policies that perform well across task instances, including those with unseen applications.

What carries the argument

The HCL-GP framework, which performs automated decomposition of successful executions to extract components, generalizes them for reuse, and employs semantic search for efficient retrieval and policy composition.

Load-bearing premise

Successful executions can be automatically decomposed into components that remain useful and correctly composable when applied to new, different task instances.

What would settle it

A test showing lower success rates when using the learned component library compared to generating policies without it on a held-out set of challenge tasks with unseen applications would indicate the claim does not hold.

Figures

Figures reproduced from arXiv: 2605.06957 by Haritha Ananthakrishnan, Harsha Kokel, Kavitha Srinivas, Michael Katz, Shirin Sohrabi.

**Figure 2.** Figure 2: Cumulative SGC per total debugging iterations on the AppWorld normal (top) and challenge (bottom) test sets. Iteration Efficiency [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗

**Figure 3.** Figure 3: Cumulative SGC as a function of inference cost for using Claude 4.6 pricing. Cost-Accuracy Trade-offs. For cost analysis, [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗

read the original abstract

We present a dynamic policy-learning approach that combines generalized planning and hierarchical task decomposition for LLM-based agents. Our method, Hierarchical Component Learning for Generalized Policies (HCL-GP ), learns parameterized policies that generalize across task instances and automatically extracts reusable components from successful executions, organizing them into a component library for compositional policy generation. We address three challenges: (1) learning components through automated decomposition, (2) generalizing components to maximize reuse, and (3) efficient retrieval via semantic search. Evaluated on the AppWorld benchmark, our approach achieves 98.2% accuracy on normal tasks and 97.8% on challenge tasks with unseen applications, improving 15.8 points over static synthesis on challenging scenarios. For open-source models, dynamic reuse enables 62.5% success versus near-zero without reuse. This demonstrates that classical planning concepts can be effectively integrated with LLM agents for improved accuracy and efficiency.

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

The paper shows that pulling reusable components out of successful LLM traces and retrieving them semantically can lift planning accuracy on AppWorld, especially for smaller models, but the gains rest on unquantified assumptions about decomposition quality and retrieval precision.

read the letter

The key point is that this work gives a concrete method for LLM agents to learn and reuse policy pieces across tasks. It extracts components automatically from good executions, stores them in a library, and pulls the right ones via semantic search to build solutions for new instances, including unseen applications. That produces the reported 98.2% on normal tasks and 97.8% on challenges, plus the 15.8-point lift over static synthesis and the jump from near-zero to 62.5% for open-source models.

Referee Report

2 major / 1 minor

Summary. The paper introduces Hierarchical Component Learning for Generalized Policies (HCL-GP), a dynamic policy-learning approach for LLM-based agents that integrates generalized planning and hierarchical task decomposition. It automatically extracts reusable parameterized components from successful execution traces, organizes them into a component library, and retrieves them via semantic search to enable compositional policy generation on new tasks, including those involving unseen applications. Evaluated on the AppWorld benchmark, the method reports 98.2% accuracy on normal tasks and 97.8% on challenge tasks (a 15.8-point gain over static synthesis), with open-source models achieving 62.5% success via dynamic reuse versus near-zero without it.

Significance. If the results hold under rigorous controls, the work provides a concrete demonstration of how classical hierarchical planning ideas can be operationalized with LLM agents to improve generalization and efficiency on complex, compositional tasks. The reported gains on challenge scenarios with unseen applications and the large lift for open-source models suggest practical utility for agentic systems, particularly where static synthesis fails. The approach's emphasis on automated decomposition and library-based reuse offers a reusable framework that could influence future LLM planning research.

major comments (2)

[Abstract] Abstract: The abstract reports strong benchmark numbers (98.2% normal-task accuracy, 97.8% on challenge tasks with unseen applications, 15.8-point gain, and 62.5% vs near-zero for open-source models) but supplies no details on experimental controls, number of runs, statistical significance tests, error bars, variance across seeds, or the precise construction of the challenge tasks. This absence makes it impossible to determine whether the gains are robust or sensitive to the specific AppWorld task distribution.
[Method] Method description (component extraction, generalization, and retrieval): The central claim that dynamic reuse drives the reported performance hinges on automated decomposition yielding components whose parameters and interfaces abstract instance-specific details, plus semantic retrieval returning the correct subset with high precision so that hierarchical re-composition does not accumulate failures. The manuscript provides no quantification of retrieval precision, component reuse frequency, or error-propagation rates during composition, leaving the 62.5% open-source success rate and the 15.8-point improvement without direct supporting measurements.

minor comments (1)

The paper would benefit from an explicit ablation isolating the contribution of the component library versus the base LLM planner, and from clearer pseudocode or a diagram showing the end-to-end flow of trace decomposition, library insertion, and semantic retrieval.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback. We address each major comment below and indicate the revisions we will incorporate to improve the manuscript.

read point-by-point responses

Referee: [Abstract] Abstract: The abstract reports strong benchmark numbers (98.2% normal-task accuracy, 97.8% on challenge tasks with unseen applications, 15.8-point gain, and 62.5% vs near-zero for open-source models) but supplies no details on experimental controls, number of runs, statistical significance tests, error bars, variance across seeds, or the precise construction of the challenge tasks. This absence makes it impossible to determine whether the gains are robust or sensitive to the specific AppWorld task distribution.

Authors: We agree that the abstract would be strengthened by additional context on the experimental setup. In the revised manuscript we will expand the abstract with a concise summary of the controls, noting that all reported accuracies are averaged across multiple independent runs with variance shown, that statistical significance was evaluated via appropriate tests, and that challenge tasks are defined as those involving applications unseen during component extraction (as detailed in the experimental section). This will make the abstract more self-contained while preserving its brevity. revision: yes
Referee: [Method] Method description (component extraction, generalization, and retrieval): The central claim that dynamic reuse drives the reported performance hinges on automated decomposition yielding components whose parameters and interfaces abstract instance-specific details, plus semantic retrieval returning the correct subset with high precision so that hierarchical re-composition does not accumulate failures. The manuscript provides no quantification of retrieval precision, component reuse frequency, or error-propagation rates during composition, leaving the 62.5% open-source success rate and the 15.8-point improvement without direct supporting measurements.

Authors: We recognize the value of direct measurements for these mechanisms. While the current manuscript already contains ablation studies comparing performance with and without the component library (including the large lift for open-source models), we will add explicit quantifications in the revision: retrieval precision (fraction of retrieved components that match task requirements), average reuse frequency per task, and an analysis of composition error rates. These metrics will be computed from the existing execution traces and presented in an expanded results subsection to directly support the performance claims. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical evaluation on fixed benchmark

full rationale

The paper presents an empirical method (HCL-GP) for learning and reusing policy decompositions with LLM agents, evaluated directly on the AppWorld benchmark. Reported accuracies (98.2% normal, 97.8% challenge) and improvements over static synthesis are measured outcomes from experimental runs, not quantities derived from equations or parameters fitted within the paper itself. No load-bearing derivation chain, self-definitional relations, or self-citation reductions appear in the abstract or described approach; the work remains self-contained against the external benchmark.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The approach rests on standard assumptions from LLM agent literature and classical planning (e.g., that successful trajectories contain extractable reusable sub-policies and that semantic similarity correlates with functional reuse). No explicit free parameters, axioms, or invented entities are stated in the abstract.

pith-pipeline@v0.9.0 · 5468 in / 1215 out tokens · 37052 ms · 2026-05-11T01:07:07.082145+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

42 extracted references · 3 canonical work pages · 1 internal anchor

[1]

Argall, Sonia Chernova, Manuela M

Brenna D. Argall, Sonia Chernova, Manuela M. Veloso, and Brett Browning. A survey of robot learning from demonstration.Robotics and Autonomous Systems, 57(5):469–483, 2009

2009
[2]

General agent evaluation

Elron Bandel, Asaf Yehudai, Lilach Eden, Yehoshua Sagron, Yotam Perlitz, Elad Venezian, Natalia Razinkov, Natan Ergas, Shlomit Shachor Ifergan, Segev Shlomov, Michal Jacovi, Leshem Choshen, Liat Ein-Dor, Yoav Katz, and Michal Shmueli-Scheuer. General agent evaluation. arXiv:2602.22953 [cs.AI], 2026. URLhttps://arxiv.org/abs/2602.22953

work page internal anchor Pith review arXiv 2026
[3]

Generalized planning: Non-deterministic abstractions and trajectory constraints

Blai Bonet, Giuseppe De Giacomo, Hector Geffner, and Sasha Rubin. Generalized planning: Non-deterministic abstractions and trajectory constraints. In Carles Sierra, editor,Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI 2017), pages 873–879. IJCAI, 2017

2017
[4]

Reinforcement learning for long-horizon interactive llm agents, 2025

Kevin Chen, Marco Cusumano-Towner, Brody Huval, Aleksei Petrenko, Jackson Hamburger, Vladlen Koltun, and Philipp Krähenbühl. Reinforcement learning for Long-Horizon interactive LLM agents. arXiv:2502.01600 [cs.LG], 2025

work page arXiv 2025
[5]

Hendler, and Dana S

Kutluhan Erol, James A. Hendler, and Dana S. Nau. HTN planning: Complexity and expressivity. InProceedings of the Twelfth National Conference on Artificial Intelligence (AAAI 1994), pages 1123–1128. AAAI Press, 1994

1994
[6]

HTN planning.Artificial Intelligence, 222:124–156, 2015

Ilche Georgievski and Marco Aiello. HTN planning.Artificial Intelligence, 222:124–156, 2015

2015
[7]

Morgan Kaufmann, 2004

Malik Ghallab, Dana Nau, and Paolo Traverso.Automated Planning: Theory and Practice. Morgan Kaufmann, 2004

2004
[8]

Exploring the use of LLMs in generalized planning

Nils Hodel. Exploring the use of LLMs in generalized planning. Bachelor’s thesis, Saarland University, 2024

2024
[9]

HTN-MAKER: Learning HTNs with minimal additional knowledge engineering required

Chad Hogg, Héctor Muñoz-Avila, and Ugur Kuter. HTN-MAKER: Learning HTNs with minimal additional knowledge engineering required. InProceedings of the Twenty-Third AAAI Conference on Artificial Intelligence (AAAI 2008), pages 950–956. AAAI Press, 2008

2008
[10]

Hierarchical reinforcement learning: A survey and open research challenges.Machine Learning and Knowledge Extraction, 4(1): 172–221, 2022

Matthias Hutsebaut-Buysse, Kevin Mets, and Steven Latré. Hierarchical reinforcement learning: A survey and open research challenges.Machine Learning and Knowledge Extraction, 4(1): 172–221, 2022

2022
[11]

A review of generalized planning

Sergio Jiménez, Javier Segovia-Aguas, and Anders Jonsson. A review of generalized planning. The Knowledge Engineering Review, 34:e5, 2019

2019
[12]

Billion-scale similarity search with GPUs

Jeff Johnson, Matthijs Douze, and Hervé Jégou. Billion-scale similarity search with GPUs. IEEE Transactions on Big Data, 7(3):535–547, 2021

2021
[13]

Towards Enterprise-Ready computer using generalist agent

Sami Marreed, Alon Oved, Avi Yaeli, Segev Shlomov, Ido Levy, Aviad Sela, Asaf Adi, and Nir Mashkif. Towards Enterprise-Ready computer using generalist agent. arXiv:2503.01861 [cs.DC], 2025

work page arXiv 2025
[14]

Nau, Tsz-Chiu Au, Okhtay Ilghami, Ugur Kuter, J

Dana S. Nau, Tsz-Chiu Au, Okhtay Ilghami, Ugur Kuter, J. William Murdock, Dan Wu, and Fusun Yaman. SHOP2: An HTN planning system.Journal of Artificial Intelligence Research, 20:379–404, 2003

2003
[15]

Generalized planning in PDDL domains with pretrained large language models

Tom Silver, Soham Dan, Kavitha Srinivas, Josh Tenenbaum, Leslie Pack Kaelbling, and Michael Katz. Generalized planning in PDDL domains with pretrained large language models. In Jennifer Dy and Sriraam Natarajan, editors,Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI 2024), pages 20256–20264. AAAI Press, 2024

2024
[16]

Improved generalized planning with LLMs through strategy refinement and reflection

Katharina Stein, Nils Hodel, Daniel Fišer, Jörg Hoffmann, Michael Katz, and Alexander Koller. Improved generalized planning with LLMs through strategy refinement and reflection. InPro- ceedings of the Thirty-Sixth International Conference on Automated Planning and Scheduling (ICAPS 2026). AAAI Press, 2026

2026
[17]

Sutton, Doina Precup, and Satinder Singh

Richard S. Sutton, Doina Precup, and Satinder Singh. Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning.Artificial Intelligence, 112: 181–211, 1999

1999
[18]

amazon : An online shopping platform where users can buy a wide variety of products

Harsh Trivedi, Tushar Khot, Mareike Hartmann, Ruskin Manku, Vinty Dong, Edward Li, Shashank Gupta, Ashish Sabharwal, and Niranjan Balasubramanian. AppWorld: A controllable world of apps and people for benchmarking interactive coding agents. In Lun-Wei Ku, Andre Martins, and Vivek Srikumar, editors,Findings of the Association for Computational Linguistics:...

2024
[19]

**API Discovery** - Find and understand unknown APIs
[20]

You can implement new components based on the task at hand

**Learned API Utilities** - Pre-discovered components you can call directly - You will be provided with the most relevant ones for your task to start with. You can implement new components based on the task at hand
[21]

your search terms

**Recommended APIs** - Recommended APIs relevant to your task. # CATEGORY 1: API Discovery You can discover APIs using: ```python # Search for APIs search_results = apis.api_docs.search_api_docs( query="your search terms", page_limit=20) # Show descriptions for all APIs available for a given app api_descriptions = apis.api_docs.show_api_descriptions( app_...
[22]

**Assume all reusable component implementations will be added to your code** (provided separately)
[23]

**Create scenario-specific helper functions** as needed
[24]

**Follow the proven structure** with clear step markers (STEP 1, STEP 2, etc.)
[25]

**Use learned utilities when available**
[26]

**Use discovery pattern for unknown operations**
[27]

="*80) print(

**Include the main policy function** that orchestrates all the steps **Structure your code like this:** ```python def scenario_X_policy(param1, param2, ...): Generalized policy for scenario X. Args: param1: Description of parameter 1 param2: Description of parameter 2 print("="*80) print("SCENARIO X: [Description]") print("="*80) # STEP 1: Discover APIs p...
[28]

Propose a minimal, stable list of policy function parameters that the runner can pass at call time
[29]

Every policy MUST accept`task_text: str`(required)
[30]

Do NOT include parameters that cannot be derived from task_text or discovered via AppWorld APIs at runtime
[31]

Keep params general across tasks in the scenario (avoid overfitting to one task)
[32]

This is mandatory

IMPORTANT: Based on each of the 3 tasks mentioned in the scenario description, assign param_values to each parameter you define, except the task_text parameter. This is mandatory
[33]

amazon : An online shopping platform where users can buy a wide variety of products

IMPORTANT: Identify which AppWorld apps are needed for this scenario. Available Apps[ "amazon : An online shopping platform where users can buy a wide variety of products.", "spotify : A music streaming platform that provides access to a vast library of songs, albums, and playlists.",...] Add an "apps_used" field to your JSON output listing the apps menti...
[34]

Use file_system to read

IMPORTANT: Plan out the high-level steps needed to accomplish the task. What are the steps you will need take to complete the task? - For each step, be as specific and detailed as possible. Include all the steps that are necessary including logging in to an app or getting the necessary credentials. - ALWAYS explicitly mention which app(s) will be used in ...
[35]

Works for all functions in the cluster
[36]

Has a generic name (e.g.,'authenticate'instead of'authenticate_venmo')
[37]

Uses parameters to handle app-specific or scenario-specific differences
[38]

Includes comprehensive docstring explaining the generalization
[39]

Maintains all functionality from the original functions **IMPORTANT - Clustering Flexibility:** - The initial clustering may not be perfect. You have the flexibility to: - **Split a cluster** if functions are too different and shouldn't be merged (keep them as separate functions) - **Merge across clusters** if you notice functions from different clusters ...
[40]

Update these single functions to call your NEW generalized functions
[41]

Adjust parameters to match the new generalized function signatures
[42]

However, if the function is actually only meant for a particular app, then keep the app name in the name of the function and include the app name in the docstring

Preserve all other functionality in these single functions ## Key Principles: **Generalization:** - Remove app-specific names when possible (venmo_login→login, with app parameter). However, if the function is actually only meant for a particular app, then keep the app name in the name of the function and include the app name in the docstring. - Use parame...