General ML · reinforcement learning papers, 2023–2026

Key findings

• General ML · reinforcement learning went from 287 papers in 2023 to 575 in 2026, roughly 2.0x in three years.
• ICLR publishes the most General ML · reinforcement learning work: 847 papers from 2023 to 2026, 51% of the four-venue total.
• The fastest-growing overlap is with Diffusion Models: papers tagged with both rose from 2 in 2023 to 34 in 2026.
• Among the 6 topics under General Machine Learning, it ranks #2 by 2026 paper count; the fastest grower in the group, General ML · evaluation, is up 4.3x versus 2.0x here.

Representative papers

Spread across years and venues so every edition shows up

• NeurIPS 2026The Sample Complexity of Multiple Change Point Identification under Bandit Feedback
• ICML 2026De-attribute to Forget for LLM Unlearning
• ICLR 2026AbstRaL: Augmenting LLMs' Reasoning by Reinforcing Abstract Thinking
• CVPR 2026Dynamic Important Example Mining for Reinforcement Finetuning
• NeurIPS 2025RULE: Reinforcement UnLEarning Achieves Forget-retain Pareto Optimality
• ICML 2025The Number of Trials Matters in Infinite-Horizon General-Utility Markov Decision Processes
• ICLR 2025A General Framework for Off-Policy Learning with Partially-Observed Reward
• CVPR 2025Automated Proof of Polynomial Inequalities via Reinforcement Learning
• NeurIPS 2024Learning General Parameterized Policies for Infinite Horizon Average Reward Constrained MDPs via Primal-Dual Policy Gradient Algorithm
• ICML 2024Provable Risk-Sensitive Distributional Reinforcement Learning with General Function Approximation
• ICLR 2024Bandits with Replenishable Knapsacks: the Best of both Worlds
• CVPR 2024Improving Unsupervised Hierarchical Representation with Reinforcement Learning
• NeurIPS 2023General Munchausen Reinforcement Learning with Tsallis Kullback-Leibler Divergence
• ICML 2023Non-stationary Reinforcement Learning under General Function Approximation
• ICLR 2023A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning
• CVPR 2023Frustratingly Easy Regularization on Representation Can Boost Deep Reinforcement Learning
• NeurIPS 2026Learning When to Stop: Selective Imitation Learning Under Arbitrary Dynamics Shift
• ICML 2026Fox in the Henhouse: Supply-Chain Backdoor Attacks Against Reinforcement Learning
• ICLR 2026APPLE: Toward General Active Perception via Reinforcement Learning
• CVPR 2026Reinforcement-Guided Synthetic Data Generation for Privacy-Sensitive Identity Recognition

All General ML · reinforcement learning papers

101 papers, page 1 of 3

1. NeurIPS 2024A Method for Evaluating Hyperparameter Sensitivity in Reinforcement Learning
2. NeurIPS 2024A Nearly Optimal and Low-Switching Algorithm for Reinforcement Learning with General Function Approximation
3. NeurIPS 2024A Study of Plasticity Loss in On-Policy Deep Reinforcement Learning
4. NeurIPS 2024A theoretical case-study of Scalable Oversight in Hierarchical Reinforcement Learning
5. NeurIPS 2024A Unified Principle of Pessimism for Offline Reinforcement Learning under Model Mismatch
6. NeurIPS 2024A2PO: Towards Effective Offline Reinforcement Learning from an Advantage-aware Perspective
7. NeurIPS 2024Achieving Constant Regret in Linear Markov Decision Processes
8. NeurIPS 2024Achieving Tractable Minimax Optimal Regret in Average Reward MDPs
9. NeurIPS 2024Adam on Local Time: Addressing Nonstationarity in RL with Relative Adam Timesteps
10. NeurIPS 2024Adaptive Exploration for Data-Efficient General Value Function Evaluations
11. NeurIPS 2024Adversarially Trained Weighted Actor-Critic for Safe Offline Reinforcement Learning
12. NeurIPS 2024Beyond Optimism: Exploration With Partially Observable Rewards
13. NeurIPS 2024CALE: Continuous Arcade Learning Environment
14. NeurIPS 2024Compositional Automata Embeddings for Goal-Conditioned Reinforcement Learning
15. NeurIPS 2024Deterministic Uncertainty Propagation for Improved Model-Based Offline Reinforcement Learning
16. NeurIPS 2024Diffusion Imitation from Observation
17. NeurIPS 2024Diffusion Policies Creating a Trust Region for Offline Reinforcement Learning
18. NeurIPS 2024Diffusion-based Curriculum Reinforcement Learning
19. NeurIPS 2024Diffusion-DICE: In-Sample Diffusion Guidance for Offline Reinforcement Learning
20. NeurIPS 2024Distributional Reinforcement Learning with Regularized Wasserstein Loss
21. NeurIPS 2024Doubly Mild Generalization for Offline Reinforcement Learning
22. NeurIPS 2024Dual Critic Reinforcement Learning under Partial Observability
23. NeurIPS 2024Dynamic Model Predictive Shielding for Provably Safe Reinforcement Learning
24. NeurIPS 2024DynaMITE-RL: A Dynamic Model for Improved Temporal Meta-Reinforcement Learning
25. NeurIPS 2024Efficient Reinforcement Learning by Discovering Neural Pathways
26. NeurIPS 2024Enhancing Robustness in Deep Reinforcement Learning: A Lyapunov Exponent Approach
27. NeurIPS 2024Exclusively Penalized Q-learning for Offline Reinforcement Learning
28. NeurIPS 2024Exploiting the Replay Memory Before Exploring the Environment: Enhancing Reinforcement Learning Through Empirical MDP Iteration
29. NeurIPS 2024Exploring the Edges of Latent State Clusters for Goal-Conditioned Reinforcement Learning
30. NeurIPS 2024Federated Ensemble-Directed Offline Reinforcement Learning
31. NeurIPS 2024First-Explore, then Exploit: Meta-Learning to Solve Hard Exploration-Exploitation Trade-Offs
32. NeurIPS 2024Foundations of Multivariate Distributional Reinforcement Learning
33. NeurIPS 2024Goal Conditioned Reinforcement Learning for Photo Finishing Tuning
34. NeurIPS 2024Goal-Conditioned On-Policy Reinforcement Learning
35. NeurIPS 2024How does Inverse RL Scale to Large State Spaces? A Provably Efficient Approach
36. NeurIPS 2024How to Solve Contextual Goal-Oriented Problems with Offline Datasets?
37. NeurIPS 2024Hybrid Reinforcement Learning Breaks Sample Size Barriers In Linear MDPs
38. NeurIPS 2024Identifying Latent State-Transition Processes for Individualized Reinforcement Learning
39. NeurIPS 2024Implicit Curriculum in Procgen Made Explicit
40. NeurIPS 2024In-Trajectory Inverse Reinforcement Learning: Learn Incrementally Before an Ongoing Trajectory Terminates
41. NeurIPS 2024Is Value Learning Really the Main Bottleneck in Offline RL?
42. NeurIPS 2024Iteratively Refined Behavior Regularization for Offline Reinforcement Learning
43. NeurIPS 2024KALM: Knowledgeable Agents by Offline Reinforcement Learning from Large Language Model Rollouts
44. NeurIPS 2024Kernel-Based Function Approximation for Average Reward Reinforcement Learning: An Optimist No-Regret Algorithm
45. NeurIPS 2024Last-Iterate Global Convergence of Policy Gradients for Constrained Reinforcement Learning
46. NeurIPS 2024Learning General Parameterized Policies for Infinite Horizon Average Reward Constrained MDPs via Primal-Dual Policy Gradient Algorithm
47. NeurIPS 2024Learning Goal-Conditioned Representations for Language Reward Models
48. NeurIPS 2024Local Linearity: the Key for No-regret Reinforcement Learning in Continuous MDPs
49. NeurIPS 2024Maximum Entropy Reinforcement Learning via Energy-Based Normalizing Flow
50. NeurIPS 2024MetaCURL: Non-stationary Concave Utility Reinforcement Learning

FAQ

How many General ML · reinforcement learning papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

1,676 in total: 287 in 2023, 403 in 2024, 411 in 2025, 575 in 2026.

How many General ML · reinforcement learning papers are at NeurIPS 2026?

NeurIPS 2026 has 113 General ML · reinforcement learning papers, versus 108 the year before.

How many General ML · reinforcement learning papers are at ICLR 2026?

ICLR 2026 has 335 General ML · reinforcement learning papers, versus 230 the year before.

How many General ML · reinforcement learning papers are at ICML 2026?

ICML 2026 has 113 General ML · reinforcement learning papers, versus 71 the year before.

How many General ML · reinforcement learning papers are at CVPR 2026?

CVPR 2026 has 14 General ML · reinforcement learning papers, versus 2 the year before.

Which conference publishes the most General ML · reinforcement learning papers?

ICLR, with 847 papers from 2023 to 2026.