ANO: Robust Policy Optimization via Bounded, Redescending Gain Fields

arXiv:2605.02320 · cs.AI, cs.LG · Submitted 2026-05-04 · Read on arXiv

cs.AI, cs.LG

Submitted: 2026-05-04

Updated: 2026-09-23

Code: https://github.com/huggingface/accelerate

Terminology

• Reinforcement Learning — 18× in this paper · explained in 4 episodes
• Robustness — 18× in this paper · explained in 5 episodes
• GRPO — 14× in this paper · explained in 6 episodes
• Alignment — 12× in this paper · explained in 1 episode
• Anchored Neighborhood Optimization — 9× in this paper
• Deep Reinforcement Learning — 8× in this paper · explained in 2 episodes
• Proximal Policy Optimization — 5× in this paper
• Fine-Tuning — 4× in this paper · explained in 5 episodes
• Benchmark — 3× in this paper · explained in 2 episodes
• Large Language Model — 3× in this paper
• Parsimony — 3× in this paper · explained in 2 episodes
• Proximal Policy Optimization (PPO) — 3× in this paper · explained in 5 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Core — 2× in this paper · explained in 2 episodes
• Expert Normalized Score — 2× in this paper
• Human Normalized Score — 2× in this paper
• Actor — 1× in this paper · explained in 3 episodes
• Adaptivity — 1× in this paper · explained in 1 episode
• Algorithmic Stability — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 3 episodes
• Convergence Guarantees — 1× in this paper · explained in 3 episodes
• Deep Reinforcement Learning (DRL) — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Large Language Model (LLM) — 1× in this paper · explained in 3 episodes
• Markov Decision Process — 1× in this paper
• Markov Decision Process (MDP) — 1× in this paper · explained in 11 episodes
• Mode Collapse — 1× in this paper · explained in 2 episodes
• Objective Function — 1× in this paper · explained in 2 episodes
• Pruning — 1× in this paper · explained in 4 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Reward Hacking — 1× in this paper · explained in 8 episodes
• Sample Efficiency — 1× in this paper · explained in 4 episodes
• Structural Constraints — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Trust Region Policy Optimization — 1× in this paper

Sources

• Training Diffusion Models with Reinforcement Learning
• Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO
• Emergence of Locomotion Behaviours in Rich Environments
• A Closer Look at Deep Policy Gradients
• DeepSeek-V3 Technical Report
• Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning
• High-Dimensional Continuous Control Using Generalized Advantage Estimation
• Proximal Policy Optimization Algorithms
• DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
• Gymnasium: A Standard Interface for Reinforcement Learning Environments

Related papers

• MAVEN-T: Reinforced Heterogeneous Distillation for Real-Time Multi-Agent Trajectory Prediction
• Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models
• The Clinician's Veto: Navigating Trust, Liability, and Uncertainty in Autonomous AI Prescribing
• MindHelper: Closed-Loop Embodied Mental-State Reasoning for Precision Intervention
• Incumbent Advantage: Brand Bias and Cognitive Manipulation Dynamics in LLM Recommendation Systems
• VSAL: A Vision Solver with Adaptive Layouts for Graph Property Detection