Gradient-Momentum Coupling: A Parameter-Space Proxy for Learning Progress

arXiv:2605.05856 · cs.LG · Submitted 2026-05-07 · Read on arXiv

cs.LG

Submitted: 2026-05-07

Updated: 2026-09-25

Code: https://github.com/benketriel/measuring-learning-rl

Terminology

• AUC — 19× in this paper · explained in 2 episodes
• Robustness — 13× in this paper · explained in 5 episodes
• Reinforcement Learning — 10× in this paper · explained in 4 episodes
• Adam — 7× in this paper · explained in 2 episodes
• Gradient-Momentum Coupling — 7× in this paper
• Actor — 4× in this paper · explained in 3 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Alignment — 3× in this paper · explained in 1 episode
• Credit assignment — 3× in this paper · explained in 3 episodes
• Distillation — 3× in this paper · explained in 2 episodes
• Failure Modes — 3× in this paper · explained in 3 episodes
• Abstraction — 2× in this paper · explained in 1 episode
• Benchmark — 2× in this paper · explained in 2 episodes
• Cosine Similarity — 2× in this paper · explained in 3 episodes
• Information Gain — 2× in this paper · explained in 2 episodes
• Intrinsic Curiosity Module — 2× in this paper
• Random Network Distillation — 2× in this paper
• Regularization — 2× in this paper · explained in 3 episodes
• Representation Learning — 2× in this paper · explained in 2 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Action Space — 1× in this paper · explained in 1 episode
• Active Learning — 1× in this paper · explained in 6 episodes
• Animat — 1× in this paper · explained in 1 episode
• Area Under Curve — 1× in this paper
• Computational overhead — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Epistemic Uncertainty — 1× in this paper · explained in 10 episodes
• Reward Shaping — 1× in this paper · explained in 2 episodes
• World Model — 1× in this paper · explained in 7 episodes

Sources

• Go-Explore: a New Approach for Hard-Exploration Problems
• Teacher-Student Curriculum Learning
• Proximal Policy Optimization Algorithms
• CurES: From Gradient Analysis to Efficient Curriculum Learning for Reasoning LLMs

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