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
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 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 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.
arXiv Machine Learning
Jul 21
Implicit Actor Critic Coupling via a Supervised Learning Framework for RLVR
arXiv:2509. 02522v3 Announce Type: replace-cross Abstract: Recent advances in Reinforcement Learning with Verifiable Rewards (RLVR) have empowered large language models (LLMs) to tackle challenging reasoning tasks such as mathematics and programming, however existing RLVR methods often suffer from sparse reward signals and unstable policy gradient updates inherent to RL-based approaches.
Hugging Face Trending Papers
Jul 28
dtControl2+$\varepsilon$: Trading Optimality for Explainability in MDPs via Decision Trees
Over the past decade, decision trees have been used to represent controllers (a. k.
arXiv AI
Jul 28
SeekJudge: A Practical Reward Framework for Reinforcement Learning in Computer-Use Agents
arXiv:2607. 23263v1 Announce Type: new Abstract: Deciding whether a trajectory actually fulfills its instruction governs how we measure computer-use agents on long-horizon graphical-user-interface tasks and how we train them with reinforcement learning.