arxiv: 2602.02799 · v2 · submitted 2026-02-02 · 💻 cs.LG · cs.AI

Joint Learning of Hierarchical Neural Options and Abstract World Model

Wasu Top Piriyakulkij , Wolfgang Lehrach , Kevin Ellis , Kevin Murphy This is my paper

Pith reviewed 2026-05-16 08:03 UTC · model grok-4.3

classification 💻 cs.LG cs.AI

keywords hierarchical optionsworld modelsreinforcement learningsample efficiencyAtari gamesskill compositionabstract modelsneural networks

0 comments p. Extension

The pith

AgentOWL jointly learns an abstract world model and hierarchical neural options to acquire skills more efficiently than model-free baselines.

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

The paper introduces AgentOWL, a method that learns an abstract world model abstracting across states and time together with hierarchical neural options in a single process. This joint training targets the long-standing goal of building agents that compose existing skills into new ones without requiring vast amounts of interaction data. Experiments focus on a subset of object-centric Atari games, where the approach acquires more skills, uses less data, and generalizes in ways that separate model-free hierarchical methods do not. A sympathetic reader would value the result because existing hierarchical reinforcement learning algorithms have been limited by high sample complexity, and a working joint model could make skill composition practical.

Core claim

We propose a novel method, which we call AgentOWL, that jointly learns -- in a sample efficient way -- an abstract world model (abstracting across both states and time) and a set of hierarchical neural options. We show, on a subset of Object-Centric Atari games, that our method can learn more skills using less data than baseline methods and possesses learning and generalization capabilities that the baselines do not have.

What carries the argument

The joint optimization of an abstract world model that abstracts across states and time together with hierarchical neural options that represent multi-level skills.

Load-bearing premise

That jointly learning the abstract world model and hierarchical neural options will produce sample-efficient skill acquisition without the abstraction or optimization process introducing biases that undermine the claimed advantages.

What would settle it

A direct comparison on the same subset of object-centric Atari games in which AgentOWL fails to learn more skills with less data or shows no improvement in learning speed and generalization over the model-free baselines would falsify the central claim.

Figures

Figures reproduced from arXiv: 2602.02799 by Kevin Ellis, Kevin Murphy, Wasu Top Piriyakulkij, Wolfgang Lehrach.

**Figure 1.** Figure 1: Illustration of hierarchical planning and execution in AgentOWL where the goal is to go to the left platform in the left room. Left: AgentOWL goes through possible plans and successfully makes a short plan (two high level steps) in its abstract world model to reach the goal. Right: AgentOWL executes a hierarchical sequence of options. (The hierarchical structure is represented by the indentation.) of which… view at source ↗

**Figure 2.** Figure 2: Illustration of how AgentOWL learns its abstract world model (left) and hierarchical options (right). Left: given a dataset of option transitions, we learn an abstract world model using (an extension of) the method of (Piriyakulkij et al., 2025). Specifically, each expert is generated using LLM code synthesis, and the weight for each expert (denoted θi) is learned using gradient descent on the likelihood o… view at source ↗

**Figure 3.** Figure 3: Fraction of options mastered vs number of environment steps for the three OCAtari’s games we test on: Montezuma’s Revenge, Pitfall, and Private Eye. Option is acquired once its success rate for the recent episodes reaches threshold δ = 0.5. Room 4 Room 3 Room 2 Room 1 Player's starting location Player can traverse between rooms through the sides Only AgentOWL master options for these red-bordered goal obje… view at source ↗

**Figure 4.** Figure 4: Screenshots of 4 rooms of Pitfall stitched together. Player starts in Room 1 (rightmost) and can traverse to other rooms through the sides of the screen. Goals that only AgentOWL masters within 5M environment steps ( [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗

**Figure 5.** Figure 5: Ablation study: Removing core components of AgentOWL lead to fewer options being acquired and/or more data from the environment being needed. Setting nthreshold = 0 means stabilization for hierarchical DQN is not implemented. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 (key) Goal Number 0.00 0.25 0.50 0.75 1.00 Success Rate Implicit Learning of Sub-options Sub-options of the option "TO_key" with random po… view at source ↗

**Figure 6.** Figure 6: Left: Illustration of the goal labels (where all goals are of the form ”touch this specific object”) and the path the player needs to take to get the key from the starting position. Right: The success rates of the sub-options implicitly trained while the agent is training for the goal ”key”, compared to those of sub-options with randomly initialized policy network. The sub-options for the sub-goals within … view at source ↗

**Figure 7.** Figure 7: Screenshots of Montezuma’s Revenge with goals labeled in order it appears in the ordered list of goals Room 4 Room 3 Room 2 Room 1 1 2 3 4 5 7 6 9 10 11 12 15 14 13 16 18 17 19 25 23 24 26 20 21 22 27 28 29 31 30 32 33 8 34 35 36 37 38 [PITH_FULL_IMAGE:figures/full_fig_p019_7.png] view at source ↗

**Figure 8.** Figure 8: Screenshots of Pitfall with goals labeled in order it appears in the ordered list of goals Room +2 Room 0 Room +1 Room +3 Room -1 Room -2 Room -3 1 2 3 4 5 6 7 8 10 9 11 12 13 14 15 16 19 17 18 20 21 22 23 24 25 27 26 28 [PITH_FULL_IMAGE:figures/full_fig_p019_8.png] view at source ↗

**Figure 9.** Figure 9: Screenshots of Private Eye with goals labeled in order it appears in the ordered list of goals 19 [PITH_FULL_IMAGE:figures/full_fig_p019_9.png] view at source ↗

read the original abstract

Building agents that can perform new skills by composing existing skills is a long-standing goal of AI agent research. Towards this end, we investigate how to efficiently acquire a sequence of skills, formalized as hierarchical neural options. However, existing model-free hierarchical reinforcement algorithms need a lot of data. We propose a novel method, which we call AgentOWL (Option and World model Learning Agent), that jointly learns -- in a sample efficient way -- an abstract world model (abstracting across both states and time) and a set of hierarchical neural options. We show, on a subset of Object-Centric Atari games, that our method can learn more skills using less data than baseline methods and possesses learning and generalization capabilities that the baselines do not have.

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

AgentOWL jointly learns abstract world models and hierarchical options for better skill acquisition on some Atari games, but the abstract gives almost no numbers or controls to judge if the joint setup actually drives the gains.

read the letter

The main takeaway is that this paper puts forward AgentOWL as a way to learn an abstract world model (over states and time) together with hierarchical neural options, aiming for sample-efficient skill learning on Object-Centric Atari. The joint training is presented as the key that lets the agent pick up more skills with less data and generalize better than baselines. That combination is the clearest new element here, since most prior work tends to handle world models and options in separate stages or with different objectives. The setup makes sense on paper: the model can support planning while the options handle composition, and doing them together might reduce the data hunger that model-free hierarchical methods usually show. The experiments are limited to a subset of those Atari games, which is a fair choice for testing object-based skills. The soft spots sit in the missing details. The abstract states superiority in skill count, data use, and generalization but supplies no concrete metrics, baseline descriptions, statistical tests, or ablation results. Without those, it is hard to tell whether the joint optimization is responsible for the reported edges or whether other implementation choices are doing the work. Generalization claims in particular need tight controls to rule out game-specific artifacts. This paper is mainly for people already working on hierarchical RL or model-based methods who want to see if combining the two at training time helps data efficiency. A reader who cares about Atari skill benchmarks would find it relevant if the full experiments hold up. It is worth sending to peer review so the experimental claims can be checked properly; the idea is coherent enough that referees could give useful feedback on the controls and ablations.

Referee Report

1 major / 2 minor

Summary. The paper proposes AgentOWL, a method that jointly learns an abstract world model (abstracting across states and time) and a set of hierarchical neural options in a sample-efficient manner. It evaluates the approach on a subset of Object-Centric Atari games, claiming that the method learns more skills using less data than baselines while exhibiting superior learning and generalization capabilities.

Significance. If the empirical results hold under rigorous controls, the work would advance hierarchical reinforcement learning by showing how joint optimization of world models and options can improve sample efficiency and enable better skill composition and generalization in complex environments.

major comments (1)

Abstract: The central claims of empirical superiority in skill count, data usage, and generalization are asserted without any reported metrics, baseline details, statistical tests, or experimental controls. This leaves the primary results without verifiable quantitative support and makes it impossible to assess whether the joint learning mechanism delivers the claimed advantages.

minor comments (2)

The abstract and title use the term 'abstract world model' without a precise definition of the abstraction mechanism (e.g., state abstraction, temporal abstraction, or both) or how it is represented.
No mention is made of the specific Object-Centric Atari games used or the choice of baselines, which are necessary for reproducibility and fair comparison.

Simulated Author's Rebuttal

1 responses · 0 unresolved

Thank you for the detailed review. We have carefully considered the major comment and provide our response below. We agree that revisions to the abstract are necessary to strengthen the presentation of our results.

read point-by-point responses

Referee: Abstract: The central claims of empirical superiority in skill count, data usage, and generalization are asserted without any reported metrics, baseline details, statistical tests, or experimental controls. This leaves the primary results without verifiable quantitative support and makes it impossible to assess whether the joint learning mechanism delivers the claimed advantages.

Authors: We thank the referee for this observation. While the manuscript body provides detailed results including quantitative metrics on skill learning, data efficiency, baseline comparisons, and generalization on Object-Centric Atari games, along with experimental controls, we agree that the abstract should include more specific support for the claims to be self-contained. In the revised version, we will update the abstract to report key metrics, such as the number of skills learned, reductions in data usage, and generalization performance, and reference the statistical tests and controls employed. This will make the empirical superiority claims verifiable from the abstract. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper describes an empirical method (AgentOWL) for jointly learning hierarchical neural options and an abstract world model, with performance claims resting on experiments in Object-Centric Atari games. No equations, parameter-fitting steps presented as predictions, self-definitional constructs, or load-bearing self-citation chains appear in the abstract or high-level description. The joint-learning mechanism is offered as an independent algorithmic contribution whose advantages are evaluated externally against baselines, rendering the derivation self-contained.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no explicit free parameters, axioms, or invented entities; standard RL assumptions such as Markovian dynamics are implicitly relied upon but not detailed.

pith-pipeline@v0.9.0 · 5422 in / 1271 out tokens · 38139 ms · 2026-05-16T08:03:03.385923+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 a novel method, which we call AgentOWL ... that jointly learns ... an abstract world model (abstracting across both states and time) and a set of hierarchical neural options.
IndisputableMonolith/Foundation/DimensionForcing.lean alexander_duality_circle_linking unclear

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

po(f′|s) using PoE-World ... each expert is a short symbolic program ... pθ(s′|s,a)=∏j p(s′j|s,a)

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.

Reference graph

Works this paper leans on

13 extracted references · 13 canonical work pages · 1 internal anchor

[1]

PMLR, 2021b. Ball, P. J., Bauer, J., Belletti, F., Brownfield, B., Ephrat, A., Fruchter, S., Gupta, A., Holsheimer, K., Holynski, A., Hron, J., et al. Genie 3: A new frontier for world models. Google DeepMind Blog, pp. 253–279, 2025. Bellemare, M. G., Naddaf, Y ., Veness, J., and Bowling, M. The arcade learning environment: An evaluation plat- form for ge...

work page arXiv 2025
[2]

The Termination Critic

PMLR, 2016. Hafner, D., Lee, K.-H., Fischer, I., and Abbeel, P. Deep hierarchical planning from pixels.Advances in Neural Information Processing Systems, 35:26091–26104, 2022. Harutyunyan, A., Dabney, W., Borsa, D., Heess, N., Munos, R., and Precup, D. The termination critic.arXiv preprint arXiv:1902.09996, 2019. Heess, N., Wayne, G., Tassa, Y ., Lillicra...

work page internal anchor Pith review Pith/arXiv arXiv 2016
[3]

AnyObjTypeTouching: The player object touches a platform object

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[4]

SpecificObjTouching: The player object touches the platform object located at (x=8, y=125)

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[5]

Now, I want you to list 4 possible features of the input list of objects has that allows us to achieve the goal of ’{goal}’

SpecificObjTouching: ... Now, I want you to list 4 possible features of the input list of objects has that allows us to achieve the goal of ’{goal}’. Input list of objects: {input} Please follow these rules for your output:

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[7]

Make the features diverse

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[8]

Do use interactions (what the player is touching), as they usually make good features

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[9]

21 Joint Learning of Hierarchical Neural Options and Abstract World Model I’ll give you an input list of objects

Each rule should of type ’AnyObjTypeTouching’ or ’SpecificObjTouching’ Table 6.Prompt for LLM to propose preconditions for games where the agent controls only the Player object: Montezuma’s Revenge and Pitfall. 21 Joint Learning of Hierarchical Neural Options and Abstract World Model I’ll give you an input list of objects. I want you to list 4 possible fe...

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[10]

RoomNumberExist: An object with type ’roomnumber_+0’ exists

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[11]

Input list of objects: {input} Please follow these rules for your output:

ObjTouchingAndRoomNumberExist: The car object touches the platform object and an object with type ’roomnumber_+0’ exists Now, I want you to list 2 possible features of the input list of objects has that allows us to achieve the goal of ’{goal}’. Input list of objects: {input} Please follow these rules for your output:

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[12]

Do not explain -- simply list each feature

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[13]

Each rule should of type ’RoomNumberExist’ or ’ObjTouchingAndRoomNumberExist’

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[14]

Make sure to mention the roomnumber in the feature, e.g., ’an object with type ’roomnumber_+0’ exists’ Table 7.Prompt for LLM to propose preconditions for games where the agent controls several objects: Private Eye (the agent controls Player and Car object) 22

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