PolyStepOR: Learning to Decide Without Optimal Decisions

arXiv:2609.32465 · cs.LG · Submitted 2026-09-26 · Read on arXiv

cs.LG

Submitted: 2026-09-26

Updated: 2026-09-26

Code: https://github.com/vietngth/polystep-or

Project page: https://vietngth.github.io/polystep-visualization

Terminology

• Regret — 86× in this paper · explained in 3 episodes
• Benchmark — 25× in this paper · explained in 2 episodes
• Core — 11× in this paper · explained in 2 episodes
• Adam — 5× in this paper · explained in 2 episodes
• Optimal Transport — 5× in this paper · explained in 5 episodes
• Alignment — 3× in this paper · explained in 1 episode
• Combinatorial Optimization — 2× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Score Function — 1× in this paper · explained in 4 episodes

Sources

• Decision-Focused On-Policy Learning for Contextual Linear Optimization with Partial Feedback
• Hyperbolic contractivity and the Hilbert metric on probability measures
• Evolution Strategies as a Scalable Alternative to Reinforcement Learning
• A Unified Analysis of Stochastic Momentum Methods for Deep Learning

Related papers

• Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
• AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
• Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
• Convergence issues in Relational Concept Analysis based on AOC-posets
• Beliefs Beyond Posteriors: Local-Consistency Optimisation for Bayesian Neural Networks
• Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks