arXiv AI
Aug 21
CharTool: Tool-Integrated Visual Reasoning for Chart Understanding
arXiv:2604. 02794v2 Announce Type: replace Abstract: Charts are ubiquitous in scientific and financial literature for presenting structured data.
Reinforcement learning
Policy optimisation, reward modelling and RLHF — how models are trained by feedback rather than by labels.
arXiv Machine Learning
Aug 21
FAR-DPO: Feasibility-Aware and Robust Direct Preference Optimization for Cyclic Peptide Design
arXiv:2608. 19808v1 Announce Type: new Abstract: Cyclic peptides are emerging as promising molecular scaffolds in drug discovery due to their high binding affinity and structural stability.
arXiv Machine Learning
Aug 21
Maximum Likelihood Reinforcement Learning
arXiv:2602. 02710v2 Announce Type: replace Abstract: Reinforcement learning (RL) is the method of choice for training models in setups where the objective function can only be evaluated by sampling from the model.
arXiv AI
Aug 21
Concentrated Liquidity Provision: a Reinforcement Learning Perspective
arXiv:2608. 19389v1 Announce Type: cross Abstract: Automated market makers (AMMs) are a cornerstone of decentralised finance (DeFi).
arXiv AI
Aug 21
Truncate Bad, Upweight Good: BoN-Style Distillation via Rank-Based Classification
arXiv:2608. 19748v1 Announce Type: cross Abstract: Inference-time selection methods, such as Best-of-N, improve generation by sampling a pool of candidates and selecting the top-ranked completion according to a reward model.
arXiv AI
Aug 21
ADAPT: Physics-Aware Diffusion-based World Models for Adaptive Predictive Transferable HVAC Control
arXiv:2608. 19804v1 Announce Type: new Abstract: Buildings account for roughly one-third of global energy consumption and CO$_2$ emissions.
arXiv AI
Aug 21
Adaptive Probabilistic Shielding by Learning MDPs for Safe Reinforcement Learning
arXiv:2608. 19836v1 Announce Type: cross Abstract: Probabilistic shielding is a technique for safe reinforcement learning (RL).
arXiv AI
Aug 21
MileGPO: Milestone Inference with Local Evidence for Graph-Based Policy Optimization of Long-Horizon LLM Agents
arXiv:2608. 19803v1 Announce Type: cross Abstract: Credit assignment is challenging in long-horizon agentic reinforcement learning, where supervision often comes only from final rewards.
arXiv Machine Learning
Aug 21
Asymptotic Theory for IV-Based Reinforcement Learning with Potential Endogeneity
arXiv:2103. 04021v4 Announce Type: replace-cross Abstract: In the standard data analysis framework, data is collected (once and for all), and then data analysis is carried out.
arXiv Machine Learning
Aug 21
Towards Formalizing Reinforcement Learning Theory: A Robbins-Siegmund Approach
arXiv:2511. 03618v2 Announce Type: replace Abstract: In this paper, we formalize the almost sure convergence of $Q$-learning and linear temporal difference (TD) learning with Markovian samples using the Lean 4 theorem prover based on the Mathlib library.
arXiv AI
Aug 21
Learning When to Think: Adaptive Reasoning for Test-Time Compute Allocation
arXiv:2608. 20256v1 Announce Type: new Abstract: Reasoning language models trained with reinforcement learning typically operate under a fixed token budget rather than an explicitly adaptive one, which can lead to over-computation on easy problems and insufficient computation on difficult ones.
arXiv AI
Aug 21
EXIMO: VLM Guided Exploration of VLA Policies
arXiv:2608. 19891v1 Announce Type: new Abstract: How to efficiently finetune robot policies to learn new tasks on the fly?
arXiv AI
Aug 21
PEA-DPO: Perception-Enhanced Alignment Direct Preference Optimization for MLLMs Alignment
arXiv:2608. 19598v1 Announce Type: cross Abstract: Direct Preference Optimization (DPO) has emerged as an effective approach for aligning large language models (LLMs) with human preferences.
arXiv Machine Learning
Aug 21
Inadvertent Context Leakage in Language Models
arXiv:2608. 19857v1 Announce Type: new Abstract: For AI agents to be useful beyond simple chat, they must hold sensitive user context such as calendars, credentials, health records, and financial data.
arXiv Machine Learning
Aug 21
Higher Resolution, Better Generalization: Unlocking Visual Scaling in Deep Reinforcement Learning
arXiv:2605. 10546v2 Announce Type: replace Abstract: Pixel-based deep reinforcement learning agents are typically trained on heavily downsampled visual observations, a convention inherited from early benchmarks rather than grounded in principled design.
arXiv Machine Learning
Aug 21
ReAugment: Model Zoo-Guided RL for Few-Shot Time Series Augmentation and Forecasting
arXiv:2409. 06282v5 Announce Type: replace Abstract: Time series forecasting, particularly in few-shot learning scenarios, is challenging due to the limited availability of high-quality training data.
arXiv Computer Vision
Aug 21
RoMAN-Flow: Taming Autoregressive Normalizing Flows for Offline Reinforcement Learning in Robotic Manipulation
arXiv:2608. 20208v1 Announce Type: new Abstract: Offline reinforcement learning improves robotic policies using previously collected data without further environment interaction.
arXiv AI
Aug 21
The Bidding Games: Reinforcement Learning for MEV Extraction on Polygon Blockchain
arXiv:2510. 14642v2 Announce Type: replace-cross Abstract: In blockchain networks, the strategic ordering of transactions within blocks has emerged as a significant source of profit extraction, known as Maximal Extractable Value (MEV).
arXiv Computer Vision
Aug 21
Zoom-IQA: Image Quality Assessment with Reliable Region-Aware Reasoning
arXiv:2601. 02918v4 Announce Type: replace Abstract: Image Quality Assessment (IQA) is a long-standing problem in computer vision.
arXiv AI
Aug 21
DARS: Dual-Level Credit Assignment RL with Structured Reasoning for Instruction-Based Image Editing
arXiv:2608. 20161v1 Announce Type: new Abstract: Instruction-based image editing uses a planner-renderer pipeline: a vision-language model (VLM) first converts the instruction into an edit plan, and a diffusion model then executes that plan.