Deep R Programming

Gotta Learn Fast: A new benchmark for generalization in RL

Automated Reinforcement Learning: An Overview

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.

rePIRL: Learn PRM with Inverse RL for LLM Reasoning

arXiv:2602. 07832v3 Announce Type: replace-cross Abstract: Process rewards have been widely used in deep reinforcement learning to improve training efficiency, reduce variance, and prevent reward hacking.

ICR-RL: Deep Reinforcement Learning via In-Context Regression

arXiv:2509. 11259v2 Announce Type: replace-cross Abstract: Recent advancements in machine learning have largely been driven by foundation models (FMs) trained on large, diverse datasets, enabling them to generalize effectively to new, related tasks.

RN-D: Discretized Categorical Actors for On-Policy Reinforcement Learning

arXiv:2601. 23075v2 Announce Type: replace Abstract: On-policy Reinforcement Learning (RL) remains a dominant paradigm for continuous control, yet standard implementations rely on Gaussian actors and relatively shallow MLP policies, often leading to brittle optimization when gradients are noisy, and policy updates must be conservative.

AutoOR: Scalably Post-training LLMs to Autoformalize Operations Research Problems

AutoOR is a scalable synthetic data generation and reinforcement learning pipeline that trains large language models to autoformalize operations research problems expressed in natural language across linear, mixed‑integer, and non‑linear categories. By generating verified training data from standard optimization forms and using solver execution feedback as a reward signal, AutoOR enables post‑training of an 8B model to achieve state‑of‑the‑art or competitive results on six established OR benchmarks, matching significantly larger frontier models. For non‑linear problems involving physical dynamics, a curriculum RL strategy bootstraps from limited initial data, making this class tractable for post‑training.

Extrapolating from Regularised Solutions for Solving Ill-Conditioned Linear Systems in Machine Learning

arXiv:2606. 30328v1 Announce Type: cross Abstract: Rapid prototyping of algorithms is a critical step in modern machine learning.

ProDVI: Programmatic Dynamics Priors for Value Network Initialization

arXiv:2608. 06015v1 Announce Type: cross Abstract: Deep Reinforcement Learning (RL) is notoriously sample inefficient.

ProDVI: Programmatic Dynamics Priors for Value Network Initialization

Deep Reinforcement Learning (RL) is notoriously sample inefficient. One contributing factor is that RL agents are typically initialized from scratch, forcing them to acquire task-relevant knowledge through online interaction.

ReTabSyn: Realistic Tabular Data Synthesis via Reinforcement Learning

arXiv:2603. 10823v2 Announce Type: replace-cross Abstract: Deep generative models can help with data scarcity and privacy by producing synthetic training data, but they struggle in low-data, imbalanced tabular settings to fully learn the complex data distribution.

ODTlearn: A Package for Learning Optimal Decision Trees for Prediction and Prescription

arXiv:2307. 15691v4 Announce Type: replace-cross Abstract: ODTlearn is an open source Python package that provides methods for learning optimal decision trees for high-stakes predictive and prescriptive tasks based on the state-of-the-art mixed-integer optimization (MIO) framework proposed in Aghaei et al.

DiscoGen: Procedural Generation of Algorithm Discovery Tasks in Machine Learning

arXiv:2603. 17863v2 Announce Type: replace-cross Abstract: Automating the development of machine learning algorithms has the potential to unlock new breakthroughs.