OpenNovelty.org

Robustness in the Face of Partial Identifiability in Reward Learning

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
Inverse Reinforcement LearningReward LearningPreference Based Reinforcement LearningTheory
Abstract:

In Reward Learning (ReL), we are given feedback on an unknown target reward, and the goal is to use this information to recover it in order to carry out some downstream application, e.g., planning. When the feedback is not informative enough, the target reward is only partially identifiable, i.e., there exists a set of rewards, called the feasible set, that are equally plausible candidates for the target reward. In these cases, the ReL algorithm might recover a reward function different from the target reward, possibly leading to a failure in the application. In this paper, we introduce a general ReL framework that permits to quantify the drop in "performance" suffered in the considered application because of identifiability issues. Building on this, we propose a robust approach to address the identifiability problem in a principled way, by maximizing the "performance" with respect to the worst-case reward in the feasible set. We then develop Rob-ReL, a ReL algorithm that applies this robust approach to the subset of ReL problems aimed at assessing a preference between two policies, and we provide theoretical guarantees on sample and iteration complexity for Rob-ReL. We conclude with some numerical simulations to illustrate the setting and empirically characterize Rob-ReL.

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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.
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Overview

Overall Novelty Assessment

The paper introduces a framework for quantifying performance degradation in reward learning applications when the target reward is only partially identifiable, and proposes a robust approach that optimizes worst-case performance over the feasible reward set. Within the taxonomy, it occupies the 'Robust Approaches to Partial Identifiability' leaf under 'Theoretical Foundations of Partial Identifiability'. Notably, this leaf contains only the original paper itself—no sibling papers are present—indicating this is a relatively sparse research direction. The parent branch contains four leaves total, with neighboring leaves addressing identifiability characterization, specialized agent models, and optimal reward selection under partial identifiability.

The taxonomy structure reveals that the paper sits within a broader theoretical foundations branch that includes work on identifiability conditions in inverse RL and multi-agent settings. Neighboring branches address reward learning from human feedback, structured reward representations, and RL under observability constraints. The 'Robust Approaches' leaf explicitly excludes 'best-case or single-reward selection methods', distinguishing it from the sibling 'Optimal Reward Selection' leaf. This positioning suggests the paper occupies a distinct methodological niche—robust worst-case optimization—that complements but differs from approaches that impose structural assumptions or select single rewards from feasible sets.

Among the three contributions analyzed, the quantitative framework examined nine candidates with one appearing to provide overlapping prior work, while the robust approach examined ten candidates with one potential refutation. The Rob-ReL algorithm examined ten candidates with none clearly refuting it, suggesting this contribution may be more novel within the limited search scope. Across all contributions, twenty-nine total candidates were examined—a modest search scale that provides useful signals but cannot claim exhaustive coverage. The presence of refutable candidates for the first two contributions indicates some conceptual overlap exists in the examined literature, though the specific algorithmic instantiation appears less anticipated.

Based on the limited search of twenty-nine candidates, the work appears to occupy a methodologically distinct position emphasizing robust optimization under partial identifiability. The sparse population of its taxonomy leaf and the absence of sibling papers suggest this specific framing is relatively underexplored, though related ideas exist in neighboring research directions. The analysis provides useful context within the examined scope but cannot definitively assess novelty against the full breadth of reward learning literature.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
29
Contribution Candidate Papers Compared
2
Refutable Paper

Research Landscape Overview

Core task: Reward learning under partial identifiability addresses the challenge of inferring reward functions when the underlying structure cannot be fully determined from available data. The field organizes into several major branches that reflect different facets of this problem. Theoretical Foundations of Partial Identifiability explores fundamental questions about what can and cannot be learned when rewards are only partially observable, including work on characterizing identifiability conditions such as Characterising Partial Identifiability[15] and robust approaches like Robustness Partial Identifiability[0]. Reward Learning from Human Feedback focuses on practical methods for extracting preferences from human input, exemplified by Direct Preference Optimization[1] and studies of human preference models. Structured Reward Representations investigates how imposing structure—such as reward machines in Learning Reward Machines[3]—can aid learning. Reinforcement Learning Settings with Observability and Feedback Constraints examines scenarios where agents face limited information, while Reward Design and Policy Optimization Methods develops algorithms that account for partial knowledge, and Specialized Applications and Extensions applies these ideas to domains ranging from safe RL to causal inference. A particularly active line of work contrasts methods that impose structural assumptions to overcome identifiability issues with those that embrace robustness under ambiguity. For instance, approaches using reward machines or causal structures attempt to reduce the hypothesis space, while robust methods seek policies that perform well across plausible reward functions. Robustness Partial Identifiability[0] sits squarely within the theoretical foundations branch, emphasizing robust strategies when rewards cannot be pinned down exactly. This contrasts with nearby efforts like Invariance Policy Optimisation[2], which leverages causal invariances, and Causal Imitation MDP[4], which uses causal models to guide learning. The interplay between these perspectives—whether to constrain the problem through structure or to optimize robustly despite ambiguity—remains a central open question, with Robustness Partial Identifiability[0] contributing formal guarantees for the latter approach.

Claimed Contributions

Quantitative framework for Reward Learning

The authors propose a general framework for Reward Learning that models feedback as constraints on the target reward and applications as loss functions, enabling quantitative analysis of performance degradation due to partial identifiability. This framework permits measuring the drop in performance suffered in applications because of identifiability issues.

9 retrieved papers
Can Refute
Robust approach for addressing partial identifiability

The authors introduce a principled robust (minimax) approach to solve Reward Learning problems by maximizing performance with respect to the worst-case reward in the feasible set. This approach provides worst-case guarantees and quantifies the uninformativeness of feedback for a given application.

10 retrieved papers
Can Refute
Rob-ReL algorithm with theoretical guarantees

The authors develop Rob-ReL, a provably efficient algorithm that applies the robust approach to Reward Learning problems aimed at assessing preferences between two policies. The algorithm provides theoretical guarantees on sample and iteration complexity that are polynomial in relevant problem parameters.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Within the taxonomy built over the current TopK core-task papers, the original paper is assigned to a leaf with no direct siblings and no cousin branches under the same grandparent topic. In this retrieved landscape, it appears structurally isolated, which is one partial signal of novelty, but still constrained by search coverage and taxonomy granularity.

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Quantitative framework for Reward Learning

The authors propose a general framework for Reward Learning that models feedback as constraints on the target reward and applications as loss functions, enabling quantitative analysis of performance degradation due to partial identifiability. This framework permits measuring the drop in performance suffered in applications because of identifiability issues.

Contribution

Robust approach for addressing partial identifiability

The authors introduce a principled robust (minimax) approach to solve Reward Learning problems by maximizing performance with respect to the worst-case reward in the feasible set. This approach provides worst-case guarantees and quantifies the uninformativeness of feedback for a given application.

Contribution

Rob-ReL algorithm with theoretical guarantees

The authors develop Rob-ReL, a provably efficient algorithm that applies the robust approach to Reward Learning problems aimed at assessing preferences between two policies. The algorithm provides theoretical guarantees on sample and iteration complexity that are polynomial in relevant problem parameters.

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