PIGDreamer: Privileged Information Guided World Models for Safe Partially Observable Reinforcement Learning

• URL: http://arxiv.org/abs/2508.02159v1
• Date: Mon, 04 Aug 2025 08:01:19 GMT
• Title: PIGDreamer: Privileged Information Guided World Models for Safe Partially Observable Reinforcement Learning
• Abstract summary: We propose a model-based safe reinforcement learning approach that leverages privileged information to enhance the agent's safety and performance.<n>Our empirical results demonstrate that our approach significantly outperforms existing methods in terms of safety and task-centric performance.
• Score: 23.384621982394673
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Partial observability presents a significant challenge for safe reinforcement learning, as it impedes the identification of potential risks and rewards. Leveraging specific types of privileged information during training to mitigate the effects of partial observability has yielded notable empirical successes. In this paper, we propose Asymmetric Constrained Partially Observable Markov Decision Processes (ACPOMDPs) to theoretically examine the advantages of incorporating privileged information. Building upon ACPOMDPs, we propose the Privileged Information Guided Dreamer, a model-based safe reinforcement learning approach that leverages privileged information to enhance the agent's safety and performance through privileged representation alignment and an asymmetric actor-critic structure. Our empirical results demonstrate that our approach significantly outperforms existing methods in terms of safety and task-centric performance. Meanwhile, compared to alternative privileged model-based reinforcement learning methods, our approach exhibits superior performance and ease of training.

Related papers

• Class-Aware Reinforcement Learning for Counterfactual Explanation Generation [0.0]
  This study explores the impact of including an instance's predicted class, alongside features derived from the predictors, in the RL state representation for generating CFEs.<n>We compare the proposed class-aware RL method with the class-blind RL method, which is similar but excludes the instance's class information from the state representation.<n>The results show that during training, class-aware RL offers benefits in terms of convergence speed, reward optimization, and episode length reduction.
  arXiv  Detail & Related papers  (2026-07-30T09:20:07Z)
• Neglected Free Lunch from Post-training: Progress Advantage for LLM Agents [15.278397068400205]
  We show that reinforcement learning post-training already provides the ingredients for effective step-level scoring.<n>We derive an implicit advantage under a general Markov decision process.<n>We validate the effectiveness of the progress advantage across three different applications.
  arXiv  Detail & Related papers  (2026-06-24T17:54:08Z)
• Towards Sample-Efficient and Stable Reinforcement Learning for LLM-based Recommendation [56.92367609590823]
  Long Chain-of-Thought (Long CoT) reasoning has shown promise in Large Language Models (LLMs)<n>We argue that Long CoT is inherently ill-suited for the sequential recommendation domain.<n>We propose RISER, a novel Reinforced Item Space Exploration framework for Recommendation.
  arXiv  Detail & Related papers  (2026-01-31T10:02:43Z)
• Breaking the Safety-Capability Tradeoff: Reinforcement Learning with Verifiable Rewards Maintains Safety Guardrails in LLMs [3.198812241868092]
  reinforcement learning with verifiable rewards (RLVR) has emerged as a promising alternative that optimize models on objectively measurable tasks.<n>We present the first comprehensive theoretical and empirical analysis of safety properties in RLVR.<n> Empirically, we conduct extensive experiments across five adversarial safety benchmarks, demonstrating that RLVR can simultaneously enhance reasoning capabilities while maintaining or improving safety guardrails.
  arXiv  Detail & Related papers  (2025-11-26T04:36:34Z)
• Benefits and Pitfalls of Reinforcement Learning for Language Model Planning: A Theoretical Perspective [52.38531288378491]
  reinforcement learning (RL) methods have substantially enhanced the planning capabilities of Large Language Models (LLMs)<n>In this work, we investigate RL's benefits and limitations through a tractable graph-based abstraction.<n>Our theoretical analyses reveal that supervised fine-tuning (SFT) may introduce co-occurrence-based spurious solutions, whereas RL achieves correct planning primarily through exploration.
  arXiv  Detail & Related papers  (2025-09-26T17:39:48Z)
• ConfClip: Confidence-Weighted and Clipped Reward for Reinforcement Learning in LLMs [32.13266235550995]
  Reinforcement learning (RL) has become a standard paradigm for refining large language models (LLMs)<n>Inspired by observations from human learning, we introduce a RL technique that integrates verifiable outcomes with the model's own confidence estimates.
  arXiv  Detail & Related papers  (2025-09-22T13:00:35Z)
• Anchoring Refusal Direction: Mitigating Safety Risks in Tuning via Projection Constraint [52.878820730054365]
  Instruction Fine-Tuning (IFT) has been widely adopted as an effective post-training strategy to enhance abilities of Large Language Models (LLMs)<n>Recent research into the internal mechanisms of LLMs has identified the refusal direction (r-direction) in the hidden states, which plays a pivotal role in governing refusal behavior.<n>To mitigate such drift, our proposed ProCon method introduces a projection-constrained loss term that regularizes the projection magnitude of each training sample's hidden state onto the r-direction.
  arXiv  Detail & Related papers  (2025-09-08T15:24:33Z)
• Personalized Exercise Recommendation with Semantically-Grounded Knowledge Tracing [54.44838681588145]
  ExRec is a framework for personalized exercise recommendation with semantically-grounded knowledge tracing.<n>We show that ExRec generalizes robustly to new, unseen questions and that it produces interpretable student learning trajectories.
  arXiv  Detail & Related papers  (2025-07-15T07:54:04Z)
• Guided Policy Optimization under Partial Observability [36.853129816484845]
  Reinforcement Learning (RL) in partially observable environments poses significant challenges due to the complexity of learning under uncertainty.<n>We introduce Guided Policy Optimization (GPO), a framework that co-trains a guider and a learner.<n>We theoretically demonstrate that this learning scheme achieves optimality comparable to direct RL, thereby overcoming key limitations inherent in existing approaches.
  arXiv  Detail & Related papers  (2025-05-21T12:01:08Z)
• Advancing Embodied Agent Security: From Safety Benchmarks to Input Moderation [52.83870601473094]
  Embodied agents exhibit immense potential across a multitude of domains.<n>Existing research predominantly concentrates on the security of general large language models.<n>This paper introduces a novel input moderation framework, meticulously designed to safeguard embodied agents.
  arXiv  Detail & Related papers  (2025-04-22T08:34:35Z)
• Probabilistic Shielding for Safe Reinforcement Learning [51.35559820893218]
  In real-life scenarios, a Reinforcement Learning (RL) agent must often also behave in a safe manner, including at training time.<n>We present a new, scalable method, which enjoys strict formal guarantees for Safe RL.<n>We show that our approach provides a strict formal safety guarantee that the agent stays safe at training and test time.
  arXiv  Detail & Related papers  (2025-03-09T17:54:33Z)
• xSRL: Safety-Aware Explainable Reinforcement Learning -- Safety as a Product of Explainability [8.016667413960995]
  We propose xSRL, a framework that integrates both local and global explanations to provide a comprehensive understanding of RL agents' behavior.<n>xSRL also enables developers to identify policy vulnerabilities through adversarial attacks, offering tools to debug and patch agents without retraining.<n>Our experiments and user studies demonstrate xSRL's effectiveness in increasing safety in RL systems, making them more reliable and trustworthy for real-world deployment.
  arXiv  Detail & Related papers  (2024-12-26T18:19:04Z)
• OpenAI o1 System Card [274.83891368890977]
  The o1 model series is trained with large-scale reinforcement learning to reason using chain of thought.<n>This report outlines the safety work carried out for the OpenAI o1 and OpenAI o1-mini models, including safety evaluations, external red teaming, and Preparedness Framework evaluations.
  arXiv  Detail & Related papers  (2024-12-21T18:04:31Z)
• Safe to Serve: Aligning Instruction-Tuned Models for Safety and Helpfulness [0.0]
  Large language models (LLMs) have demonstrated remarkable capabilities in complex reasoning and text generation.<n>LLMs can inadvertently generate unsafe or biased responses when prompted with problematic inputs.<n>This research addresses the critical challenge of developing language models that generate both helpful and harmless content.
  arXiv  Detail & Related papers  (2024-11-26T06:52:22Z)
• Active Learning for Robust and Representative LLM Generation in Safety-Critical Scenarios [32.16984263644299]
  Large Language Models (LLMs) can generate valuable data for safety measures, but often exhibit distributional biases. We propose a novel framework that integrates active learning with clustering to guide LLM generation. Our results show that the proposed framework produces a more representative set of safety scenarios without requiring prior knowledge of the underlying data distribution.
  arXiv  Detail & Related papers  (2024-10-14T21:48:14Z)
• Bi-Factorial Preference Optimization: Balancing Safety-Helpfulness in Language Models [94.39278422567955]
  Fine-tuning large language models (LLMs) on human preferences has proven successful in enhancing their capabilities.<n>However, ensuring the safety of LLMs during fine-tuning remains a critical concern.<n>We propose a supervised learning framework called Bi-Factorial Preference Optimization (BFPO) to address this issue.
  arXiv  Detail & Related papers  (2024-08-27T17:31:21Z)
• Inverse-RLignment: Large Language Model Alignment from Demonstrations through Inverse Reinforcement Learning [62.05713042908654]
  We introduce Alignment from Demonstrations (AfD), a novel approach leveraging high-quality demonstration data to overcome these challenges.<n>We formalize AfD within a sequential decision-making framework, highlighting its unique challenge of missing reward signals.<n> Practically, we propose a computationally efficient algorithm that extrapolates over a tailored reward model for AfD.
  arXiv  Detail & Related papers  (2024-05-24T15:13:53Z)
• Feasibility Consistent Representation Learning for Safe Reinforcement Learning [25.258227763316228]
  We introduce a novel framework named Feasibility Consistent Safe Reinforcement Learning (FCSRL) This framework combines representation learning with feasibility-oriented objectives to identify and extract safety-related information from the raw state for safe RL. Our method is capable of learning a better safety-aware embedding and achieving superior performance than previous representation learning baselines.
  arXiv  Detail & Related papers  (2024-05-20T01:37:21Z)
• Online Safety Property Collection and Refinement for Safe Deep Reinforcement Learning in Mapless Navigation [79.89605349842569]
  We introduce the Collection and Refinement of Online Properties (CROP) framework to design properties at training time. CROP employs a cost signal to identify unsafe interactions and use them to shape safety properties. We evaluate our approach in several robotic mapless navigation tasks and demonstrate that the violation metric computed with CROP allows higher returns and lower violations over previous Safe DRL approaches.
  arXiv  Detail & Related papers  (2023-02-13T21:19:36Z)
• Certifying Safety in Reinforcement Learning under Adversarial Perturbation Attacks [23.907977144668838]
  We propose a partially-supervised reinforcement learning (PSRL) framework that takes advantage of an additional assumption that the true state of the POMDP is known at training time. We present the first approach for certifying safety of PSRL policies under adversarial input perturbations, and two adversarial training approaches that make direct use of PSRL.
  arXiv  Detail & Related papers  (2022-12-28T22:33:38Z)
• Assured Learning-enabled Autonomy: A Metacognitive Reinforcement Learning Framework [4.427447378048202]
  Reinforcement learning (RL) agents with pre-specified reward functions cannot provide guaranteed safety across variety of circumstances. An assured autonomous control framework is presented in this paper by empowering RL algorithms with metacognitive learning capabilities.
  arXiv  Detail & Related papers  (2021-03-23T14:01:35Z)
• SAMBA: Safe Model-Based & Active Reinforcement Learning [59.01424351231993]
  SAMBA is a framework for safe reinforcement learning that combines aspects from probabilistic modelling, information theory, and statistics. We evaluate our algorithm on a variety of safe dynamical system benchmarks involving both low and high-dimensional state representations. We provide intuition as to the effectiveness of the framework by a detailed analysis of our active metrics and safety constraints.
  arXiv  Detail & Related papers  (2020-06-12T10:40:46Z)
• Privileged Information Dropout in Reinforcement Learning [56.82218103971113]
  Using privileged information during training can improve the sample efficiency and performance of machine learning systems. In this work, we investigate Privileged Information Dropout (pid) for achieving the latter which can be applied equally to value-based and policy-based reinforcement learning algorithms.
  arXiv  Detail & Related papers  (2020-05-19T05:32:33Z)

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.