Lever: Inference-Time Policy Reuse under Support Constraints

Ihor Vitenko; Noha Ibrahim; Sihem Amer-Yahia

arxiv: 2604.20174 · v2 · submitted 2026-04-22 · 💻 cs.LG

Lever: Inference-Time Policy Reuse under Support Constraints

Ihor Vitenko , Noha Ibrahim , Sihem Amer-Yahia This is my paper

Pith reviewed 2026-05-10 00:21 UTC · model grok-4.3

classification 💻 cs.LG

keywords inference-time policy reusereinforcement learningpolicy compositionsupport constraintsbehavioral embeddingsoffline Q-value compositionGridWorld environments

0 comments

The pith

Inference-time composition of pre-trained RL policies can match or exceed training-from-scratch performance under support constraints.

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

The paper asks whether high-quality policies for new composite reinforcement learning objectives can be assembled entirely at inference time from a library of existing policies, without any further environment interaction. It presents LEVER, which retrieves candidate policies, scores them via behavioral embeddings, and builds a new policy through offline Q-value composition. The central result is that this succeeds in deterministic GridWorld tasks precisely when the pre-trained policies cover the required transitions, often matching or beating policies trained from scratch while delivering large speedups. The approach fails, however, when tasks involve long-horizon dependencies that would need value propagation across missing transitions.

Core claim

LEVER retrieves relevant policies from a library, evaluates them using behavioral embeddings, and composes new policies via offline Q-value composition. We focus on the support-limited regime where no value propagation is possible, and show that effectiveness depends critically on the coverage of available transitions. Experiments in deterministic GridWorld environments demonstrate that inference-time composition can match and in some cases exceed training-from-scratch performance while providing substantial speedups, although performance degrades when long-horizon dependencies require value propagation.

What carries the argument

Offline Q-value composition of retrieved policies, guided by behavioral embeddings and controlled exploration strategies, operating strictly within the support-limited regime that prohibits value propagation.

If this is right

When transition coverage is adequate, inference-time composition equals or surpasses from-scratch training quality.
Composition delivers large reductions in wall-clock time compared with retraining a policy for each new objective.
Performance collapses exactly when the task requires value propagation over unsupported state-action pairs.
Strategies that limit the number of candidate policies explored allow explicit trade-offs between quality and computation.

Where Pith is reading between the lines

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

Policy libraries could be pre-built for families of related tasks to enable rapid offline adaptation in robotics or game domains.
Hybrid methods that add limited online fine-tuning might recover performance when coverage is only partial.
The same retrieval-plus-composition pattern could be tested in model-based planning or imitation-learning settings where support constraints also arise.

Load-bearing premise

The library of pre-trained policies supplies transition coverage sufficient for the new objective so that value propagation is never required.

What would settle it

Apply LEVER to a deterministic GridWorld task whose optimal path requires a long chain of transitions absent from every policy in the library; the composed policy should then underperform a policy trained from scratch on that task.

Figures

Figures reproduced from arXiv: 2604.20174 by Ihor Vitenko, Noha Ibrahim, Sihem Amer-Yahia.

**Figure 1.** Figure 1: High-level overview of lever. A user specifies a task in natural language. lever retrieves relevant policies from a pretrained policy database, evaluates candidates offline using 𝜋2VEC embeddings, and composes policies offline when needed. Composition is restricted to (𝑠, 𝑎) ∈ U∩, ensuring that Q-values are combined only over transitions that are jointly observed. This constraint reflects the core limitati… view at source ↗

**Figure 2.** Figure 2: lever execution pipeline. TC and HC restrict composition to selected base policies, while EC enumerates all combinations. HC and EC evaluate composed policies offline and select the best candidate. Composition (HC), and Exhaustive Composition (EC). These strategies allow us to analyze how increasing the breadth of exploration affects performance under the offline constraint. Our evaluation is structured a… view at source ↗

**Figure 3.** Figure 3: Performance predictor fit for 16 × 16 (𝛾 = 0) across different training horizons and budgets. The horizontal axis represents the ground-truth reward, while the vertical axis shows the predicted reward using the histogram-based gradient boosting regressor [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗

**Figure 4.** Figure 4: Hybrid top-𝑘 sweep. Top: 8 × 8; bottom: 16 × 16. This confirms that when relevant transitions are present in the policy library, offline composition can effectively recover high-quality policies. Each figure also reports an upper bound, obtained by directly evaluating all available policy snapshots on the composite task. This upper bound represents the best performance achievable given the available suppo… view at source ↗

**Figure 8.** Figure 8: Average episodic return (left) and offline composi [PITH_FULL_IMAGE:figures/full_fig_p009_8.png] view at source ↗

**Figure 7.** Figure 7: Average episodic return (left) and offline composi [PITH_FULL_IMAGE:figures/full_fig_p009_7.png] view at source ↗

read the original abstract

Reinforcement learning (RL) policies are typically trained for fixed objectives, making reuse difficult when task requirements change. We study inference-time policy reuse: given a library of pre-trained policies and a new composite objective, can a high-quality policy be constructed entirely offline, without additional environment interaction? We introduce lever (Leveraging Efficient Vector Embeddings for Reusable policies), an end-to-end framework that retrieves relevant policies, evaluates them using behavioral embeddings, and composes new policies via offline Q-value composition. We focus on the support-limited regime, where no value propagation is possible, and show that the effectiveness of reuse depends critically on the coverage of available transitions. To balance performance and computational cost, lever proposes composition strategies that control the exploration of candidate policies. Experiments in deterministic GridWorld environments show that inference-time composition can match, and in some cases exceed, training-from-scratch performance while providing substantial speedups. At the same time, performance degrades when long-horizon dependencies require value propagation, highlighting a fundamental limitation of offline reuse.

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

LEVER gives a concrete offline way to retrieve and compose pre-trained policies for new objectives in gridworlds when transitions are covered, but it stays limited to deterministic cases and cannot handle value propagation.

read the letter

The key point here is that LEVER lets you reuse a library of policies for new tasks at inference time through retrieval and offline Q-value composition, but only when the available transitions cover what you need and without relying on value propagation. The new part is the full pipeline: pulling relevant policies, scoring them with behavioral embeddings, and then composing under explicit support constraints. They also add strategies to limit how many candidates you explore to keep computation reasonable. This is distinct from standard transfer learning because it happens entirely offline after the initial training. The experiments in deterministic GridWorld environments are the main evidence. They show that when coverage is good, the composed policy can match or even beat training a new policy from scratch, and it does so with big speedups. The paper is upfront that performance drops when long-horizon dependencies come into play because you can't propagate values offline. One soft spot is the narrow scope. Everything is deterministic and grid-based, so it is not clear how this would hold up in stochastic or continuous settings. The abstract does not give specifics on the baselines used or whether the results include statistical tests, which makes it harder to judge if the speedups are reliable across runs. The weakest assumption is that the library has enough coverage; if it does not, the method fails, and the paper acknowledges this but does not explore ways to handle partial coverage. This work is aimed at people in reinforcement learning who deal with changing objectives and want to avoid retraining every time. Someone studying policy composition or offline RL would find the empirical demonstration useful for understanding the boundaries of reuse. The paper shows clear thinking about its own limitations and builds on prior ideas without circularity. It deserves a serious referee because the framework is concrete and the results, while limited, are presented with the right caveats. I would recommend putting it through peer review rather than desk rejecting it.

Referee Report

2 major / 2 minor

Summary. The paper proposes LEVER, a framework for inference-time policy reuse in RL: given a library of pre-trained policies and a new composite objective, it retrieves policies via behavioral embeddings and composes them through offline Q-value composition without further environment interaction. The work focuses on the support-limited regime (no value propagation) and conditions success on transition coverage. Experiments in deterministic GridWorld environments claim that the approach can match or exceed training-from-scratch performance with substantial speedups, while performance degrades for long-horizon tasks requiring value propagation.

Significance. If the GridWorld results hold under the stated coverage conditions, the framework offers a practical route to offline policy composition that avoids retraining costs. The explicit scoping to support-limited regimes and acknowledgment of long-horizon limitations strengthen the contribution by avoiding over-claims. Reproducible code or parameter-free derivations are not mentioned, so significance rests primarily on the empirical demonstration of speedups under controlled conditions.

major comments (2)

[Experiments] Experiments section: the claim that inference-time composition matches or exceeds training-from-scratch performance lacks reported details on the number of independent runs, statistical tests (e.g., t-tests or confidence intervals), or exact baseline implementations (e.g., whether composition strategies were chosen post-hoc or fixed a priori). This makes it difficult to assess whether reported speedups are robust or sensitive to strategy selection.
[Support-limited regime] Support-limited regime discussion: the paper states that effectiveness depends critically on transition coverage, yet no quantitative threshold or coverage metric (e.g., fraction of state-action pairs covered) is provided to delineate when reuse succeeds versus degrades. This leaves the central empirical claim load-bearing on an unquantified assumption.

minor comments (2)

[Abstract and Introduction] The abstract and introduction use 'lever' and 'LEVER' interchangeably; standardize capitalization and acronym usage throughout.
[Method] Behavioral embeddings are central to retrieval but their exact construction (e.g., architecture, training objective) is referenced without a dedicated equation or pseudocode block; add a short formal definition.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback and positive overall assessment of our work on LEVER. We address each major comment below, indicating the revisions we will incorporate into the manuscript.

read point-by-point responses

Referee: [Experiments] Experiments section: the claim that inference-time composition matches or exceeds training-from-scratch performance lacks reported details on the number of independent runs, statistical tests (e.g., t-tests or confidence intervals), or exact baseline implementations (e.g., whether composition strategies were chosen post-hoc or fixed a priori). This makes it difficult to assess whether reported speedups are robust or sensitive to strategy selection.

Authors: We appreciate the referee's point on improving statistical transparency. In the revised manuscript, we will explicitly report the number of independent runs, include confidence intervals alongside means, describe the exact baseline implementations with fixed a priori hyperparameters, and add the results of appropriate statistical tests (such as t-tests) comparing LEVER to training-from-scratch. These additions will be placed in the Experiments section and figure captions to clarify robustness. revision: yes
Referee: [Support-limited regime] Support-limited regime discussion: the paper states that effectiveness depends critically on transition coverage, yet no quantitative threshold or coverage metric (e.g., fraction of state-action pairs covered) is provided to delineate when reuse succeeds versus degrades. This leaves the central empirical claim load-bearing on an unquantified assumption.

Authors: We agree that quantifying coverage strengthens the central claim. We will define and introduce a coverage metric (the fraction of state-action pairs in the target task covered by the policy library) in the revised manuscript. This metric will be reported for each GridWorld experiment, along with a discussion of how performance varies with coverage levels, to better delineate success conditions in the support-limited regime. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper presents a framework proposal (LEVER) for inference-time policy reuse via retrieval, behavioral embeddings, and offline Q-value composition, validated empirically in deterministic GridWorld environments. No mathematical derivation chain is described that reduces by construction to fitted parameters, self-definitions, or self-citation load-bearing premises; the abstract and method explicitly condition success on transition coverage and note degradation for long-horizon cases, keeping the contribution self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The abstract describes an empirical framework without explicit mathematical axioms, free parameters, or new invented entities beyond standard RL concepts such as pre-trained policies and Q-values.

axioms (1)

domain assumption Support-limited regime where no value propagation is possible
Explicitly stated as the focus of the study in the abstract.

pith-pipeline@v0.9.0 · 5478 in / 1168 out tokens · 40442 ms · 2026-05-10T00:21:02.074959+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

18 extracted references · 18 canonical work pages · 1 internal anchor

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Scarpellini, G., Konyushkova, K., Fantacci, C., Paine, T. L., Chen, Y., and Denil, M. 𝜋2vec: Policy representation with successor features. InInternational Conference on Learning Representations (ICLR), 2024. URL https://openreview.net/forum?id= o5Bqa4o5Mi. Poster

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work page 1996

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Toolorchestra: Elevating intelligence via efficient model and tool orchestration

Su, H., Diao, S., Lu, X., Liu, M., Xu, J., Dong, X., Fu, Y., Belcak, P., Ye, H., Yin, H., Dong, Y., Bakhturina, E., Yu, T., Choi, Y., Kautz, J., and Molchanov, P. Toolorches- tra: Elevating intelligence via efficient model and tool orchestration, 2025. URL https://arxiv.org/abs/2511.21689

work page arXiv 2025

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work page 2018

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Taylor, M. E. and Stone, P. Transfer learning for reinforcement learning domains: A survey.Journal of Machine Learning Research, 10(7), 2009

work page 2009

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N., Bischl, B., and Torgo, L

Vanschoren, J., Van Rijn, J. N., Bischl, B., and Torgo, L. Openml: networked science in machine learning.ACM SIGKDD Explorations Newsletter, 15(2):49–60, 2014

work page 2014

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and Mahmoud, Q

Vithayathil Varghese, N. and Mahmoud, Q. H. A survey of multi-task deep reinforcement learning.Electronics, 9(9):1363, 2020

work page 2020

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and Goel, A

Von Hessling, A. and Goel, A. K. Abstracting reusable cases from reinforcement learning. InICCBR Workshops, pp. 227–236, 2005

work page 2005

[17] [17]

Towards sample efficient reinforcement learning

Yu, Y. Towards sample efficient reinforcement learning. InIJCAI, pp. 5739–5743, 2018

work page 2018

[18] [18]

Reusable reinforcement learning via shallow trails.IEEE transactions on neural networks and learning systems, 29(6): 2204–2215, 2018

Yu, Y., Chen, S.-Y., Da, Q., and Zhou, Z.-H. Reusable reinforcement learning via shallow trails.IEEE transactions on neural networks and learning systems, 29(6): 2204–2215, 2018. 11

work page 2018