Policy optimisation, reward modelling and RLHF — how models are trained by feedback rather than by labels.
arXiv:2607. 17855v1 Announce Type: new Abstract: Bayesian inference provides a principled foundation for reasoning under uncertainty, but its computational cost hinders deployment on resource-constrained edge devices.
arXiv:2607. 16412v1 Announce Type: new Abstract: Current benchmarks for language models primarily evaluate execution on fully specified tasks.
arXiv:2607. 18163v1 Announce Type: cross Abstract: PPO and the GRPO baseline studied here use clipped surrogate objectives whose favorable-direction saturation introduces an abrupt change in the scalar objective's derivative.
arXiv:2607. 17038v1 Announce Type: new Abstract: This paper addresses key technical challenges in current large language model (LLM) agent applications, including long-horizon planning, sparse reward attribution, and dynamic environmental interaction, by designing and optimizing an intelligent agent workflow.
arXiv:2607. 16819v1 Announce Type: new Abstract: In recent years, large-scale vision-language models have been driving a paradigm shift in intelligent remote sensing image interpretation.
arXiv:2607. 16421v1 Announce Type: new Abstract: It has long been recognized that humans have the ability to switch between fast, reactive decision-making and slower, deliberative planning.
arXiv:2201. 05000v3 Announce Type: replace-cross Abstract: Reinforcement Learning and, recently, Deep Reinforcement Learning are popular methods for solving sequential decision-making problems modeled as Markov Decision Processes.
arXiv:2605. 25739v2 Announce Type: replace Abstract: We prove that no reinforcement learning policy with confidence-gated autonomy can simultaneously achieve maximum helpfulness, optimal calibration, and full autonomy under rational oversight, whenever some tasks exceed the agent's reliable competence: the Behavioral Credibility Trilemma.
arXiv:2607. 16999v1 Announce Type: cross Abstract: The Credit Assignment Problem (CAP) is fundamental to developing efficient and explainable Reinforcement Learning (RL) agents.
arXiv:2603. 06009v2 Announce Type: replace Abstract: An agent's performance stagnating at a suboptimal level is a common problem in deep on-policy RL.
arXiv:2607. 18091v1 Announce Type: cross Abstract: Structural fidelity is essential to scientific methodology diagrams.
arXiv:2607. 17760v1 Announce Type: cross Abstract: Inverse reinforcement learning (IRL) provides a powerful framework for learning from demonstrations.
arXiv:2607. 16986v1 Announce Type: new Abstract: This paper introduces Periodic Bootstrap Thompson Sampling (PBTS), an innovative extension of the classic Thompson Sampling (TS) algorithm tailored for bandit problems with periodic non-stationarity.
arXiv:2607. 18110v1 Announce Type: new Abstract: Reinforcement learning (RL) on open-ended tasks compresses an LLM's rubric-based evaluation into a scalar reward, discarding rich textual feedback and conflating responses with distinct quality profiles.
arXiv:2607. 17946v1 Announce Type: cross Abstract: Large Language Models (LLMs) often struggle to navigate value conflicts when trained with the compressed scalar rewards of Reinforcement Learning from Human Feedback (RLHF).
arXiv:2607. 17201v1 Announce Type: cross Abstract: In this work we study the Best Policy Identification (BPI) problem in online, tabular Reinforcement Learning.
arXiv:2607. 18004v1 Announce Type: new Abstract: Many visual reinforcement learning (RL) algorithms learn representations by matching latent distances to a behavioral distance induced by reward and transition similarity.
arXiv:2607. 17572v1 Announce Type: new Abstract: Group Relative Policy Optimization (GRPO) is a powerful reinforcement learning algorithm for aligning generative models with human preferences.
arXiv:2607. 16858v1 Announce Type: cross Abstract: Across environments with mixed sources of uncertainty, unsupervised reinforcement learning requires intrinsic motivation that does not precommit to a particular direction of surprise.
arXiv:2607. 17823v1 Announce Type: new Abstract: Reinforcement Learning is a cornerstone technique for modern large reasoning models.