PlatoLTL: Learning to Generalize Across Symbols in LTL Instructions for Multi-Task RL

Alessandro Abate; Ioannis Havoutis; Jacques Cloete; Mathias Jackermeier

arxiv: 2601.22891 · v2 · submitted 2026-01-30 · 💻 cs.LG

PlatoLTL: Learning to Generalize Across Symbols in LTL Instructions for Multi-Task RL

Jacques Cloete , Mathias Jackermeier , Ioannis Havoutis , Alessandro Abate This is my paper

Pith reviewed 2026-05-16 09:14 UTC · model grok-4.3

classification 💻 cs.LG

keywords LTLreinforcement learningmulti-task RLzero-shot generalizationtemporal logicparameterized propositionssymbol generalization

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The pith

By modeling LTL propositions as parameterized instances of atomic predicates, PlatoLTL lets RL policies generalize zero-shot to entirely new symbol vocabularies.

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

Multi-task reinforcement learning agents often fail when faced with LTL task descriptions that use propositions never seen during training. PlatoLTL solves this by treating each proposition as a parameterized version of a shared atomic predicate, so the policy learns common features across symbols instead of memorizing each one separately. A new architecture embeds these parameterized propositions and composes them according to the structure of the full LTL formula. Policies trained this way can immediately handle tasks whose propositions belong to a different vocabulary. The method is demonstrated across several challenging environments where both formula structure and symbol sets vary.

Core claim

The paper establishes that representing propositions as parameterized instances of atomic predicates allows a policy to learn shared structure across related propositions. This parameterization, combined with an architecture that embeds and composes the propositions into LTL formulae, produces zero-shot generalization both compositionally across LTL structures and parametrically across unseen proposition vocabularies.

What carries the argument

A neural architecture that embeds each parameterized proposition and then composes these embeddings to represent full LTL formulae.

If this is right

Policies execute tasks specified by LTL formulae that contain novel propositions without retraining.
Generalization occurs simultaneously across formula composition and across symbol sets.
The same training process yields agents that work in multiple environments with varying proposition vocabularies.

Where Pith is reading between the lines

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

The same parameterization idea could be applied to other formal task languages that use atomic events.
Agents trained this way might adapt when the set of observable high-level events grows or changes during deployment.
Robotics settings with evolving sensor vocabularies could benefit from training once on a core set of predicates.

Load-bearing premise

Propositions share enough underlying structure that treating them as parameterized instances of the same atomic predicates lets the policy transfer knowledge to unseen symbols.

What would settle it

A policy trained with PlatoLTL fails to solve any LTL task whose propositions have no parametric relation to the training set, such as switching from color-based block predicates to entirely new sensor-based predicates.

read the original abstract

A central challenge in multi-task reinforcement learning (RL) is to train generalist policies capable of performing tasks not seen during training. To facilitate such generalization, linear temporal logic (LTL) has emerged as a powerful formalism for specifying structured, temporally extended tasks to RL agents. While existing approaches to LTL-guided multi-task RL demonstrate generalization across LTL specifications, they are unable to generalize to unseen vocabularies of propositions (or "symbols"), which describe high-level events in LTL. We present PlatoLTL, a novel approach that enables policies to zero-shot generalize not only compositionally across LTL structures, but also parametrically across propositions. We model propositions as parameterized instances of atomic predicates, allowing policies to learn shared structure across related propositions. We propose a novel architecture that embeds and composes parameterized propositions to represent LTL formulae, and demonstrate zero-shot generalization in a range of challenging environments.

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

PlatoLTL adds parameterization of propositions to let LTL policies generalize to new symbols, but the abstract supplies no results or architecture details to check if zero-shot actually works.

read the letter

The main point is that this paper tries to fix a clear limitation in LTL-guided multi-task RL: existing methods handle new formula structures but not new proposition symbols. PlatoLTL models propositions as parameterized instances of atomic predicates and adds an embedding-composition architecture on top, with the claim that this produces zero-shot generalization to unseen vocabularies in addition to compositional generalization across LTL operators. They report tests in a range of environments, which at least shows they moved beyond toy cases.

Referee Report

2 major / 1 minor

Summary. The paper introduces PlatoLTL for multi-task RL with LTL-specified tasks. It claims that by modeling propositions as parameterized instances of atomic predicates, a novel embedding and composition architecture enables policies to zero-shot generalize both compositionally across LTL formulae and parametrically to entirely unseen proposition vocabularies, with empirical demonstration across challenging environments.

Significance. If the zero-shot parametric generalization claim holds with the required architectural and parameterization details, the result would meaningfully advance LTL-guided RL by removing the fixed-vocabulary restriction that limits prior work, enabling more flexible multi-task policies for temporally extended tasks.

major comments (2)

[Section 4] Section 4 and the architecture description: the parameterization of propositions is introduced without explicit bounds, continuity assumptions, or embedding-function properties on the parameter space. This leaves the zero-shot claim for arbitrary unseen symbols dependent on empirical interpolation rather than guaranteed extrapolation, as out-of-distribution parameters could yield arbitrary embeddings with no structural guarantee of compositionality.
[Experimental evaluation] The experimental evaluation (results and ablation sections): no quantitative details are provided on how zero-shot performance on novel proposition symbols is measured, including the distribution shift between training and test vocabularies, the number of unseen symbols tested, or controls that isolate parametric generalization from compositional generalization.

minor comments (1)

[Abstract] The abstract and introduction use 'parameterized instances of atomic predicates' without an early formal definition or example; adding a short illustrative example of a parameterized proposition would improve readability.

Simulated Author's Rebuttal

2 responses · 0 unresolved

Thank you for your constructive feedback on our paper. We address each of the major comments below and describe the revisions we plan to make to the manuscript.

read point-by-point responses

Referee: [Section 4] Section 4 and the architecture description: the parameterization of propositions is introduced without explicit bounds, continuity assumptions, or embedding-function properties on the parameter space. This leaves the zero-shot claim for arbitrary unseen symbols dependent on empirical interpolation rather than guaranteed extrapolation, as out-of-distribution parameters could yield arbitrary embeddings with no structural guarantee of compositionality.

Authors: We concur that the manuscript does not provide theoretical guarantees or explicit assumptions like bounds and continuity on the parameter space, making the zero-shot generalization empirical. In the revised version, we will expand Section 4 to specify the parameter ranges and generation process for propositions in our environments, clarify that generalization relies on the learned embedding function's ability to interpolate and extrapolate within the tested distributions, and add a limitations discussion on the lack of formal extrapolation guarantees. revision: yes
Referee: [Experimental evaluation] The experimental evaluation (results and ablation sections): no quantitative details are provided on how zero-shot performance on novel proposition symbols is measured, including the distribution shift between training and test vocabularies, the number of unseen symbols tested, or controls that isolate parametric generalization from compositional generalization.

Authors: The referee is correct that more quantitative details are needed. We will revise the experimental evaluation section to include specifics on the distribution shift (e.g., training parameter ranges vs. test), the exact number of unseen symbols tested across environments, and additional ablation studies or controls designed to isolate the contribution of parametric generalization from compositional generalization. revision: yes

Circularity Check

0 steps flagged

No significant circularity; parameterization is an explicit modeling choice with empirical support

full rationale

The paper's central claim rests on the architectural decision to model propositions as parameterized instances of atomic predicates, which is introduced as a novel design choice rather than derived from or reducing to any fitted parameters, self-citations, or prior results by the authors. No equations, uniqueness theorems, or load-bearing self-citations are invoked that would make the zero-shot generalization equivalent to the inputs by construction. The generalization across unseen vocabularies is presented as an empirical outcome of the embedding and composition architecture, not a self-definitional prediction. This is a standard non-circular finding for an RL architecture paper whose claims are validated through experiments rather than algebraic reduction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 1 invented entities

Abstract provides no explicit free parameters, axioms, or invented entities beyond the high-level modeling choice of parameterized propositions.

invented entities (1)

parameterized propositions no independent evidence
purpose: to represent propositions as instances of atomic predicates so that shared structure can be learned across symbols
Introduced in the abstract as the modeling step that enables generalization to unseen vocabularies.

pith-pipeline@v0.9.0 · 5465 in / 1058 out tokens · 23698 ms · 2026-05-16T09:14:45.838781+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 model propositions as parameterized instances of atomic predicates... predicate f and parameters xf... parameter embedding network ρf... fusion network ff... ϕ(p)=ff(ϕprd(f)∥ρf(xf))
IndisputableMonolith/Foundation/ArithmeticFromLogic.lean LogicNat embedding and recovery unclear

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

We propose a novel architecture that embeds and composes parameterized propositions to represent LTL formulae

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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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contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
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