ODRL Policy Comparison Through Normalisation
- URL: http://arxiv.org/abs/2603.12926v1
- Date: Fri, 13 Mar 2026 12:09:22 GMT
- Title: ODRL Policy Comparison Through Normalisation
- Abstract summary: The ODRL language has become the standard for representing policies and regulations for digital rights.<n>Its complexity is a barrier to its usage, which has caused many related theoretical and practical works to focus on different, and not interoperable, fragments of ODRL.<n>We tackle these problems by proposing an approach that involves a parametrised normalisation of ODRL policies into its minimal components.
- Score: 0.07646713951724009
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The ODRL language has become the standard for representing policies and regulations for digital rights. However its complexity is a barrier to its usage, which has caused many related theoretical and practical works to focus on different, and not interoperable, fragments of ODRL. Moreover, semantically equivalent policies can be expressed in numerous different ways, which makes comparing them and processing them harder. Building on top of a recently defined semantics, we tackle these problems by proposing an approach that involves a parametrised normalisation of ODRL policies into its minimal components which reformulates policies with permissions and prohibitions into policies with permissions exclusively, and simplifies complex logic constraints into simple ones. We provide algorithms to compute a normal form for ODRL policies and simplifying numerical and symbolic constraints. We prove that these algorithms preserve the semantics of policies, and analyse the size complexity of the result, which is exponential on the number of attributes and linear on the number of unique values for these attributes. We show how this makes complex policies representable in more basic fragments of ODRL, and how it reduces the problem of policy comparison to the simpler problem of checking if two rules are identical.
Related papers
- Policy-Conditioned Policies for Multi-Agent Task Solving [53.67744322553693]
In this work, we propose a paradigm shift that bridges the gap by representing policies as human-interpretable source code.<n>We reformulate the learning problem by utilizing Large Language Models (LLMs) as approximate interpreters.<n>We formalize this process as textitProgrammatic Iterated Best Response (PIBR), an algorithm where the policy code is optimized by textual gradients.
arXiv Detail & Related papers (2025-12-24T07:42:10Z) - Learning General Policies with Policy Gradient Methods [11.393603788068775]
provable correct policies that generalize over all instances of a given domain have been learned using methods.<n>The aim of this work is to bring these two research threads together to illuminate the conditions under which (deep) reinforcement learning approaches can be used.<n>We draw on lessons learned from previous and deep learning approaches, and extend them in a convenient way.
arXiv Detail & Related papers (2025-12-22T13:08:58Z) - Random Policy Valuation is Enough for LLM Reasoning with Verifiable Rewards [47.557539197058496]
We introduce Random Policy Valuation for Diverse Reasoning (ROVER)<n>ROVER is a minimalist yet highly effective RL method that samples actions from a softmax over uniform-policy Q-values.<n>It demonstrates superior performance in both textbfquality (textbf+8.2 on pass@1, textbf+16.8 on pass@256) and textbfdiversity (textbf+17.6%)
arXiv Detail & Related papers (2025-09-29T16:09:07Z) - Group-Relative REINFORCE Is Secretly an Off-Policy Algorithm: Demystifying Some Myths About GRPO and Its Friends [64.71326476563213]
Off-policy reinforcement learning for large language models (LLMs) is attracting growing interest.<n>We present a first-principles derivation for grouprelative REINFORCE without assuming a specific training data distribution.<n>This perspective yields two general principles for adapting REINFORCE to off-policy settings.
arXiv Detail & Related papers (2025-09-29T02:34:54Z) - Few-shot Policy (de)composition in Conversational Question Answering [54.259440408606515]
We propose a neuro-symbolic framework to detect policy compliance using large language models (LLMs) in a few-shot setting.<n>We show that our approach soundly reasons about policy compliance conversations by extracting sub-questions to be answered, assigning truth values from contextual information, and explicitly producing a set of logic statements from the given policies.<n>We apply this approach to the popular PCD and conversational machine reading benchmark, ShARC, and show competitive performance with no task-specific finetuning.
arXiv Detail & Related papers (2025-01-20T08:40:15Z) - Explainable Finite-Memory Policies for Partially Observable Markov Decision Processes [1.0499611180329806]
Partially Observable Markov Decision Processes (POMDPs) are a fundamental framework for decision-making under uncertainty and partial observability.
We provide a representation of such policies, both (i) in an interpretable formalism and (ii) typically of smaller size, together yielding higher explainability.
arXiv Detail & Related papers (2024-11-20T14:42:23Z) - Statistical Analysis of Policy Space Compression Problem [54.1754937830779]
Policy search methods are crucial in reinforcement learning, offering a framework to address continuous state-action and partially observable problems.
Reducing the policy space through policy compression emerges as a powerful, reward-free approach to accelerate the learning process.
This technique condenses the policy space into a smaller, representative set while maintaining most of the original effectiveness.
arXiv Detail & Related papers (2024-11-15T02:46:55Z) - REBEL: Reinforcement Learning via Regressing Relative Rewards [59.68420022466047]
We propose REBEL, a minimalist RL algorithm for the era of generative models.<n>In theory, we prove that fundamental RL algorithms like Natural Policy Gradient can be seen as variants of REBEL.<n>We find that REBEL provides a unified approach to language modeling and image generation with stronger or similar performance as PPO and DPO.
arXiv Detail & Related papers (2024-04-25T17:20:45Z) - Hundreds Guide Millions: Adaptive Offline Reinforcement Learning with
Expert Guidance [74.31779732754697]
We propose a novel plug-in approach named Guided Offline RL (GORL)
GORL employs a guiding network, along with only a few expert demonstrations, to adaptively determine the relative importance of the policy improvement and policy constraint for every sample.
Experiments on various environments suggest that GORL can be easily installed on most offline RL algorithms with statistically significant performance improvements.
arXiv Detail & Related papers (2023-09-04T08:59:04Z) - Preliminary Results on Using Abstract AND-OR Graphs for Generalized
Solving of Stochastic Shortest Path Problems [25.152899734616298]
Shortest Path Problems (SSPs) are goal-oriented problems in the real-world.
A key difficulty for computing solutions for SSPs is finding solutions to even moderately sized problems intractable.
We show that our approach can be embedded in any SSP solver to compute hierarchically optimal policies.
arXiv Detail & Related papers (2022-04-08T21:30:47Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.