arXiv AI
Jul 9
ORCAID: Oblique Rule-Based Continuous-Action Interpretation for Deep RL Policies
arXiv:2607. 07235v1 Announce Type: cross Abstract: Explainability remains a key issue in reinforcement learning (RL).
Interpretable reinforcement learning with decision-tree pruning
arXiv:2608. 07151v1 Announce Type: cross Abstract: Reinforcement learning policies are difficult to inspect, but interpreting them is a prerequisite for trustworthiness.
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arXiv Machine Learning
Jun 4
Explainably Safe Reinforcement Learning
arXiv:2606. 04634v1 Announce Type: new Abstract: Trust in a decision-making system requires both safety guarantees and the ability to interpret and understand its behavior.
arXiv AI
Sep 25
Auditability Is Not One Property: Rule Overlap, Behavioural Agreement, and Composition in Reinforcement Learning
The paper introduces a protocol for auditing and composing reinforcement‑learning policies using discrete behavioral rules, defining auditability through six testable predicates such as trace integrity and rule coverage. Experiments show that overlapping rule sets do not guarantee behavioral agreement, and that rule‑based fusion often fails to outperform value‑based composition, highlighting limitations in current description layers. The authors provide an evidence‑bounded audit framework and outline future directions for more robust skill composition.
arXiv AI
Jun 9
Distilling LLM Reasoning into an Interpretable Policy Tree for Human-AI Collaboration
arXiv:2606. 08596v1 Announce Type: new Abstract: Constructing efficient and reliable policies to assist humans is indispensable for human-AI collaboration.
arXiv Machine Learning
Jun 9
CATPO: Critique-Augmented Tree Policy Optimization
arXiv:2606. 08346v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has become a dominant paradigm for improving the reasoning capabilities of large language models (LLMs).
arXiv Machine Learning
Sep 3
Objective-Behavior Alignment: Diagnostics for MORL Policy Selection
The paper introduces a diagnostic workflow for multi‑objective reinforcement learning (MORL) that reveals behavioral differences among policies on the Pareto front, which are not apparent from value vectors alone. It offers quantitative and visual tools to inspect these variations and demonstrates their effectiveness on both simple grid tasks and more complex continuous‑control benchmarks.
arXiv AI
Jun 8
Exploring Agentic Tool-Calling Decisions via Uncertainty-Aligned Reinforcement Learning
arXiv:2606. 06976v1 Announce Type: new Abstract: Large language model (LLM)-based agents often make suboptimal tool-use decisions, including unsupported tool invocation and hallucinated direct responses, which may accumulate errors throughout multi-step interactions.
arXiv Machine Learning
Aug 27
Training Alignment Auditors via Reinforcement Learning
The paper presents a reinforcement learning approach to enhance large language model (LLM) auditors for alignment tasks. By training policies that investigate target models for hidden behaviors and using an LLM judge to compare investigations, the method improves audit realism and reduces false positives. Experiments show better performance on adversarially fine‑tuned targets and a low false‑positive rate below 1%.
arXiv AI
Jul 29
dtControl2+$\varepsilon$: Trading Optimality for Explainability in MDPs via Decision Trees
arXiv:2607. 25925v1 Announce Type: new Abstract: Over the past decade, decision trees have been used to represent controllers (a.
arXiv Machine Learning
Jul 17
Branching Policy Optimization: Sandbox-Native Language Agent Reinforcement Learning
arXiv:2607. 14171v1 Announce Type: new Abstract: Reinforcement learning has emerged as the dominant paradigm for training large language model (LLM) agents that interact with executable sandboxes.
arXiv Machine Learning
Jun 17
Performance-Driven Environment Abstraction with Multi-Timescale Learning
arXiv:2606. 17377v1 Announce Type: new Abstract: We study performance-driven environment abstraction for decision-making in large Markov decision processes.
arXiv AI
Aug 12
SPOTting the Future: Lookahead Explanations for Deep Reinforcement Learning
arXiv:2608. 09967v1 Announce Type: new Abstract: Deep reinforcement learning (DRL) agents achieve strong performance in complex environments, yet their decision-making processes remain difficult to interpret.