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Online Decision Making with Generative Action Sets

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.8Download Report PDF
online decision makingcreate-to-use
Abstract:

With advances in generative AI, decision-making agents can now dynamically create new actions during online learning, but action generation typically incurs costs that must be balanced against potential benefits. We study an online learning problem where an agent can generate new actions at any time step by paying a one-time cost, with these actions becoming permanently available for future use. The challenge lies in learning the optimal sequence of two-fold decisions: which action to take and when to generate new ones, further complicated by the triangular tradeoffs among exploitation, exploration and creation. To solve this problem, we propose a doubly-optimistic algorithm that employs Lower Confidence Bounds (LCB) for action selection and Upper Confidence Bounds (UCB) for action generation. Empirical evaluation on healthcare question-answering datasets demonstrates that our approach achieves favorable generation-quality trade-offs compared to baseline strategies. From theoretical perspectives, we prove that our algorithm achieves the optimal regret of O(Tdd+2ddd+2+dTlog⁡T)O(T^{\frac{d}{d+2}}d^{\frac{d}{d+2}} + d\sqrt{T\log T}), providing the first sublinear regret bound for online learning with expanding action spaces.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper introduces a create-to-reuse framework where an agent dynamically generates new actions during online learning by paying one-time costs, balancing exploitation, exploration, and creation. It resides in the Curriculum-Based and Progressive Action Space Growth leaf, which contains only two papers including this one. This leaf sits under Action Space Expansion Mechanisms and Theoretical Foundations, indicating a relatively sparse research direction focused on structured, incremental action space growth rather than reactive or application-driven expansion strategies.

The taxonomy reveals neighboring branches addressing related but distinct challenges: Lifelong and Continual Learning with Changing Action Sets handles catastrophic forgetting across evolving action spaces, while Adaptive Resolution and Discretization Strategies adjust granularity rather than generating entirely new actions. The paper's focus on cost-aware action generation distinguishes it from these directions, which either assume costless expansion or fixed discretization schemes. The broader Action Space Expansion Mechanisms branch emphasizes theoretical regret bounds and algorithmic mechanisms, positioning this work within foundational rather than application-specific research.

Among 29 candidates examined across three contributions, none were found to clearly refute the proposed approach. The create-to-reuse formulation examined 10 candidates with no refutable overlap, the doubly-optimistic algorithm examined 10 candidates with no refutable overlap, and the optimal regret bound examined 9 candidates with no refutable overlap. This limited search scope suggests that within the top-30 semantically similar papers, the specific combination of cost-aware action generation, doubly-optimistic confidence bounds, and sublinear regret guarantees appears underexplored, though the analysis does not cover the full literature landscape.

Based on the limited search scope of 29 candidates, the work appears to occupy a relatively novel position within its immediate research neighborhood. The sparse population of its taxonomy leaf and absence of refutable prior work among examined candidates suggest potential originality, though a more exhaustive literature review would be needed to confirm whether similar cost-aware generation frameworks exist in adjacent domains or under different terminologies.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
29
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: online learning with dynamically expanding action spaces. This field addresses scenarios where an agent must learn effective policies even as the set of available actions grows or changes over time. The taxonomy organizes research into several main branches: theoretical foundations and expansion mechanisms that formalize how action spaces evolve; composite and structured decomposition methods that break large or complex action sets into manageable subspaces; transfer learning and model reuse strategies that leverage prior knowledge when new actions appear; constrained and masked approaches that selectively enable or disable actions; application-driven studies spanning domains such as robotics, scheduling, and network control; time-varying system modeling for environments with inherent temporal dynamics; and optimization techniques that expand search spaces adaptively. Representative works like Growing Action Spaces[10] and Changing Action Set[21] illustrate early efforts to handle action-set variability, while more recent studies such as Actor-Critic Reuse[3] and Action-Adaptive Continual[36] explore how to efficiently transfer learned components across evolving action configurations. A particularly active line of work focuses on curriculum-based and progressive growth strategies, where action spaces expand gradually to facilitate learning. Generative Action Sets[0] sits within this branch, emphasizing mechanisms that generate or reveal new actions in a structured manner rather than presenting the full action space at once. This contrasts with methods like Growing Q-Networks[28] and Adaptive Action Space[24], which dynamically adjust network architectures or action representations in response to observed task demands. Meanwhile, composite decomposition approaches such as Composite Action Space[2] tackle the combinatorial challenge of large action sets by factoring them into smaller components, and application-driven studies like Flingbot[5] demonstrate how domain-specific constraints shape expansion policies in robotic manipulation. The interplay between these directions highlights a central trade-off: whether to expand action spaces proactively via curriculum design or reactively based on environmental feedback, and how to balance exploration of new actions against exploitation of known strategies.

Claimed Contributions

Create-to-reuse problem formulation with expanding action spaces

The authors introduce a novel online learning framework where agents can dynamically generate new actions at a fixed one-time cost, with generated actions becoming permanently available for future reuse. This formulation captures triangular tradeoffs among exploitation, exploration, and creation, distinguishing it from traditional fixed-action-space settings.

10 retrieved papers
Doubly-optimistic algorithm using LCB and UCB

The authors develop an algorithm that employs LCB for action selection to balance exploitation and exploration, while using UCB-based probabilistic decisions for action generation. This double optimism principle enables the algorithm to maximize long-term value of new actions while controlling worst-case regret.

10 retrieved papers
Optimal regret bound with matching lower bound

The authors prove their algorithm achieves expected regret of O(T^(d/(d+2)) d^(d/(d+2)) + d√(T log T)) where T is the time horizon and d is the covariate dimension. They establish this is optimal by proving a matching lower bound, providing the first sublinear regret guarantee for online learning with expanding action spaces.

9 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Create-to-reuse problem formulation with expanding action spaces

The authors introduce a novel online learning framework where agents can dynamically generate new actions at a fixed one-time cost, with generated actions becoming permanently available for future reuse. This formulation captures triangular tradeoffs among exploitation, exploration, and creation, distinguishing it from traditional fixed-action-space settings.

Contribution

Doubly-optimistic algorithm using LCB and UCB

The authors develop an algorithm that employs LCB for action selection to balance exploitation and exploration, while using UCB-based probabilistic decisions for action generation. This double optimism principle enables the algorithm to maximize long-term value of new actions while controlling worst-case regret.

Contribution

Optimal regret bound with matching lower bound

The authors prove their algorithm achieves expected regret of O(T^(d/(d+2)) d^(d/(d+2)) + d√(T log T)) where T is the time horizon and d is the covariate dimension. They establish this is optimal by proving a matching lower bound, providing the first sublinear regret guarantee for online learning with expanding action spaces.

Table of Contents

Online Decision Making with Generative Action Sets | Novelty Validation