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

468 papers, page 2 of 10

51. NeurIPS 2026Learning to Cut: Reinforcement Learning for Benders Decomposition
52. NeurIPS 2026Learning to Explore with Parameter-Space Noise: A Deep Dive into Parameter-Space Noise for Reinforcement Learning with Verifiable Rewards
53. NeurIPS 2026Learning to Surpass: Training Tool-Using Agents with Anchored Feedback
54. NeurIPS 2026Learning to Trigger: Reinforcement Learning at the Large Hadron Collider
55. NeurIPS 2026Learning to Undo: Transfer Reinforcement Learning under State Space Transformations
56. NeurIPS 2026Learning When to Stop: Selective Imitation Learning Under Arbitrary Dynamics Shift
57. NeurIPS 2026Learning When to Think: Adaptive Internal Computation for Reinforcement Learning
58. NeurIPS 2026Long-Horizon Q-Learning: Accurate Value Learning via n-Step Inequalities
59. NeurIPS 2026Loop-Free Inverse Reinforcement Learning via Sequential Value Recovery
60. NeurIPS 2026LoopRPT: Reinforcement Pre-Training for Looped Language Models
61. NeurIPS 2026MaxIM: Maximally Informative Incremental Summarization via Reinforcement Learning
62. NeurIPS 2026NASDAQ: Normalized Observation Space Dynamics-Augmented Q-Learning
63. NeurIPS 2026Nemotron-Cascade: Scaling Cascaded Reinforcement Learning for General-Purpose Reasoning Models
64. NeurIPS 2026Noisy Data is Destructive to Reinforcement Learning with Verifiable Rewards
65. NeurIPS 2026Offline Constrained Reinforcement Learning under Partial Data Coverage
66. NeurIPS 2026On the Complexity of Offline Reinforcement Learning with Q*-Approximation and Partial Coverage
67. NeurIPS 2026One for All: A Non-Linear Transformer can enable Cross-Domain Generalization for In-Context Reinforcement Learning
68. NeurIPS 2026Predictive but Not Plannable: RC-aux for Latent World Models
69. NeurIPS 2026Provably Safe, Yet Performant Reinforcement Learning
70. NeurIPS 2026Quantile Geometry Regularization for Distributional Reinforcement Learning
71. NeurIPS 2026Quantile-Coupled Flow Matching for Distributional Reinforcement Learning
72. NeurIPS 2026Quotient-Categorical Representations for Bellman-Compatible Average-Reward Distributional Reinforcement Learning
73. NeurIPS 2026Reinforcement Learning Agents Are Swimmers
74. NeurIPS 2026Representation Learning Enables Scalable Multitask Deep Reinforcement Learning
75. NeurIPS 2026Reward Hacking in Rubric-Based Reinforcement Learning
76. NeurIPS 2026Reward Inflation: A Healthy Stimulus for Reinforcement Learning
77. NeurIPS 2026Reward-Conditioned Reinforcement Learning
78. NeurIPS 2026Reward-Estimated Hypergradient for Bilevel Reinforcement Learning with Black-Box Follower
79. NeurIPS 2026Robust Offline Reinforcement Learning against Out-of-Distribution Dynamics in Autonomous Driving
80. NeurIPS 2026SAIR: Cost-Efficient Multi-Stage ML Pipeline Autoscaling via In-Context Reinforcement Learning
81. NeurIPS 2026Sampling-Based Safe Reinforcement Learning
82. NeurIPS 2026SEAR: Sample Efficient Action Chunking Reinforcement Learning
83. NeurIPS 2026SIRAS: Sibling-Relative Advantage Shaping for Reinforcement Learning from Verifiable Rewards
84. NeurIPS 2026SLOPE: Optimistic Potential Landscape Shaping for Model-based Reinforcement Learning
85. NeurIPS 2026Soft Forward-Backward Representations for Zero-shot Reinforcement Learning with General Utilities
86. NeurIPS 2026Spectral Representations for Provably Robust Offline Meta-Reinforcement Learning from Bagged Rewards
87. NeurIPS 2026Split-RL: Local Conflict Resolution in Reinforcement Learning
88. NeurIPS 2026STAPO: Stabilizing Reinforcement Learning for LLMs by Silencing Rare Spurious Tokens
89. NeurIPS 2026Statistical Complexity of Soft Bellman Residual Minimization
90. NeurIPS 2026Temporal Consistency Improves Generalization in Contextual Offline Meta Reinforcement Learning
91. NeurIPS 2026TGRL: Temperature-Grouped Reinforcement Learning for Efficient Exploration in LLMs
92. NeurIPS 2026The Cost of Mismatch: Noise Amplification in Zeroth-Order Reinforcement Learning
93. NeurIPS 2026The Laplacian Keyboard: Beyond the Linear Span
94. NeurIPS 2026The Mirage of Optimizing Training Policies: Monotonic Inference Policies as the Real Objective for LLM Reinforcement Learning
95. NeurIPS 2026The Reasoning Boundary Paradox: How Reinforcement Learning Constrains Language Models
96. NeurIPS 2026The Sample Complexity of Multiple Change Point Identification under Bandit Feedback
97. NeurIPS 2026There are Levels to It: Red Teaming LLMs with Hierarchical Reinforcement Learning
98. NeurIPS 2026ToolSearcher: Optimizing Tool Selection at Scale via Reinforcement Learning
99. NeurIPS 2026Towards Convergence of PPO: An Approximate Descent Approach
100. NeurIPS 2026Towards Differentially Private Reinforcement Learning with General Function Approximation

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.